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#greenhouseforcing — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #greenhouseforcing, aggregated by home.social.

  1. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  2. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  3. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

  4. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  5. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  6. @chrisnelder
    Indeed the oil industry may suffer from lower prices.

    I am taking your first project, modifying it a bit: i intend to crunch the numbers on how much CO2 emissions will be avoided by the breakdown intended by the US administration.
    Economic recession might slow down greenhouse growth as observed during the COVID lockdown: data.yt/projections/2024-resul

    @martinvermeer @gwagner

    #GreenHouseForcing #climateChange #climateBreakdown #emissions #trade #imports #exports #oilAndGas #energy

  7. @chrisnelder
    Indeed the oil industry may suffer from lower prices.

    I am taking your first project, modifying it a bit: i intend to crunch the numbers on how much CO2 emissions will be avoided by the breakdown intended by the US administration.
    Economic recession might slow down greenhouse growth as observed during the COVID lockdown: data.yt/projections/2024-resul

    @martinvermeer @gwagner

    #GreenHouseForcing #climateChange #climateBreakdown #emissions #trade #imports #exports #oilAndGas #energy

  8. @chrisnelder
    Indeed the oil industry may suffer from lower prices.

    I am taking your first project, modifying it a bit: i intend to crunch the numbers on how much CO2 emissions will be avoided by the breakdown intended by the US administration.
    Economic recession might slow down greenhouse growth as observed during the COVID lockdown: data.yt/projections/2024-resul

    @martinvermeer @gwagner

    #GreenHouseForcing #climateChange #climateBreakdown #emissions #trade #imports #exports #oilAndGas #energy

  9. @chrisnelder
    Indeed the oil industry may suffer from lower prices.

    I am taking your first project, modifying it a bit: i intend to crunch the numbers on how much CO2 emissions will be avoided by the breakdown intended by the US administration.
    Economic recession might slow down greenhouse growth as observed during the COVID lockdown: data.yt/projections/2024-resul

    @martinvermeer @gwagner

  10. @chrisnelder
    Indeed the oil industry may suffer from lower prices.

    I am taking your first project, modifying it a bit: i intend to crunch the numbers on how much CO2 emissions will be avoided by the breakdown intended by the US administration.
    Economic recession might slow down greenhouse growth as observed during the COVID lockdown: data.yt/projections/2024-resul

    @martinvermeer @gwagner

    #GreenHouseForcing #climateChange #climateBreakdown #emissions #trade #imports #exports #oilAndGas #energy

  11. Changing our diets and ways of producing food is crucial to combat climate change and increase human health. A key change is lessening the impact of meat and animal products like eggs and dairy. 🧵

  12. @energyecon

    The top 20 highest greenhouse forcing entities collectively accounted for 17.5 GtCO2e in emissions in 2023. The list is dominated by state-owned entities, which make up 16 of the top 20, and includes a significant presence of Chinese entities, eight of which accounted for 17.3% of global fossil fuel and cement #CO2 emissions in 2023.
    carbonmajors.org/briefing/The-

    #coal #carbon #fossil #energy #CarbonMajors #globalWarming #GHG #climateCollapse #climateChange #oilAndGas #greenhouseForcing

  13. @energyecon

    The top 20 highest greenhouse forcing entities collectively accounted for 17.5 GtCO2e in emissions in 2023. The list is dominated by state-owned entities, which make up 16 of the top 20, and includes a significant presence of Chinese entities, eight of which accounted for 17.3% of global fossil fuel and cement #CO2 emissions in 2023.
    carbonmajors.org/briefing/The-

    #coal #carbon #fossil #energy #CarbonMajors #globalWarming #GHG #climateCollapse #climateChange #oilAndGas #greenhouseForcing

  14. @energyecon

    The top 20 highest greenhouse forcing entities collectively accounted for 17.5 GtCO2e in emissions in 2023. The list is dominated by state-owned entities, which make up 16 of the top 20, and includes a significant presence of Chinese entities, eight of which accounted for 17.3% of global fossil fuel and cement #CO2 emissions in 2023.
    carbonmajors.org/briefing/The-

    #coal #carbon #fossil #energy #CarbonMajors #globalWarming #GHG #climateCollapse #climateChange #oilAndGas #greenhouseForcing

  15. @energyecon

    The top 20 highest greenhouse forcing entities collectively accounted for 17.5 GtCO2e in emissions in 2023. The list is dominated by state-owned entities, which make up 16 of the top 20, and includes a significant presence of Chinese entities, eight of which accounted for 17.3% of global fossil fuel and cement emissions in 2023.
    carbonmajors.org/briefing/The-

  16. @energyecon

    The top 20 highest greenhouse forcing entities collectively accounted for 17.5 GtCO2e in emissions in 2023. The list is dominated by state-owned entities, which make up 16 of the top 20, and includes a significant presence of Chinese entities, eight of which accounted for 17.3% of global fossil fuel and cement #CO2 emissions in 2023.
    carbonmajors.org/briefing/The-

    #coal #carbon #fossil #energy #CarbonMajors #globalWarming #GHG #climateCollapse #climateChange #oilAndGas #greenhouseForcing

  17. We would halve #methane emissions from North America if we cut cattle by half 🧵

    A most efficient path would be to target #USA because the headcount is biggest and because each bovine emits most if bred the American way. Same as in Brazil: mas.to/@maugendre/113992940508

    Ref: data.yt/

    #livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #climateBreakdown #cattle #footprint #agriculture #USPol #climateChange #farming #meat #climateCollapse

  18. We would halve #methane emissions from North America if we cut cattle by half 🧵

    A most efficient path would be to target #USA because the headcount is biggest and because each bovine emits most if bred the American way. Same as in Brazil: mas.to/@maugendre/113992940508

    Ref: data.yt/

    #livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #climateBreakdown #cattle #footprint #agriculture #USPol #climateChange #farming #meat #climateCollapse

  19. We would halve #methane emissions from North America if we cut cattle by half 🧵

    A most efficient path would be to target #USA because the headcount is biggest and because each bovine emits most if bred the American way. Same as in Brazil: mas.to/@maugendre/113992940508

    Ref: data.yt/

    #livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #climateBreakdown #cattle #footprint #agriculture #USPol #climateChange #farming #meat #climateCollapse

  20. We would halve emissions from North America if we cut cattle by half 🧵

    A most efficient path would be to target because the headcount is biggest and because each bovine emits most if bred the American way. Same as in Brazil: mas.to/@maugendre/113992940508

    Ref: data.yt/

  21. We would halve #methane emissions from North America if we cut cattle by half 🧵

    A most efficient path would be to target #USA because the headcount is biggest and because each bovine emits most if bred the American way. Same as in Brazil: mas.to/@maugendre/113992940508

    Ref: data.yt/

    #livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #climateBreakdown #cattle #footprint #agriculture #USPol #climateChange #farming #meat #climateCollapse

  22. @climate @agriculture

    Greenhouse gas from livestock digestion in Equatorial Climates 🧵

    Along the Equator, it mainly is sheep and bovines that account for methane emissions.
    The biggest producers are Ethiopia and Sudan (South Sudan included).

    Reference: #GreenHouseForcing data.yt/

    #Africa #livestock #emissions #cattle #sheep #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #footprint #agriculture #Indonesia

  23. @climate @agriculture

    Greenhouse gas from livestock digestion in Equatorial Climates 🧵

    Along the Equator, it mainly is sheep and bovines that account for methane emissions.
    The biggest producers are Ethiopia and Sudan (South Sudan included).

    Reference: #GreenHouseForcing data.yt/

    #Africa #livestock #emissions #cattle #sheep #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #footprint #agriculture #Indonesia

  24. @climate @agriculture

    Greenhouse gas from livestock digestion in Equatorial Climates 🧵

    Along the Equator, it mainly is sheep and bovines that account for methane emissions.
    The biggest producers are Ethiopia and Sudan (South Sudan included).

    Reference: #GreenHouseForcing data.yt/

    #Africa #livestock #emissions #cattle #sheep #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #footprint #agriculture #Indonesia

  25. @climate @agriculture

    Greenhouse gas from livestock digestion in Equatorial Climates 🧵

    Along the Equator, it mainly is sheep and bovines that account for methane emissions.
    The biggest producers are Ethiopia and Sudan (South Sudan included).

    Reference: data.yt/

  26. @climate @agriculture

    Greenhouse gas from livestock digestion in Equatorial Climates 🧵

    Along the Equator, it mainly is sheep and bovines that account for methane emissions.
    The biggest producers are Ethiopia and Sudan (South Sudan included).

    Reference: #GreenHouseForcing data.yt/

    #Africa #livestock #emissions #cattle #sheep #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #footprint #agriculture #Indonesia

  27. @food @ecology 🧵

    #Cattle husbandry emits #greenhouse gas.
    For example:
    * South America and the Indian subcontinent breed most bovines who emit most methane.
    * China and the Middle East produce too much livestock methane in regard to their bovine headcounts.

    Emissions of #methane from livestock digestion per world region:

    #Livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #China #India #Pakistan #MiddleEast #footprint

  28. @food @ecology 🧵

    #Cattle husbandry emits #greenhouse gas.
    For example:
    * South America and the Indian subcontinent breed most bovines who emit most methane.
    * China and the Middle East produce too much livestock methane in regard to their bovine headcounts.

    Emissions of #methane from livestock digestion per world region:

    #Livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #China #India #Pakistan #MiddleEast #footprint

  29. @food @ecology 🧵

    #Cattle husbandry emits #greenhouse gas.
    For example:
    * South America and the Indian subcontinent breed most bovines who emit most methane.
    * China and the Middle East produce too much livestock methane in regard to their bovine headcounts.

    Emissions of #methane from livestock digestion per world region:

    #Livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #China #India #Pakistan #MiddleEast #footprint

  30. @food @ecology 🧵

    husbandry emits gas.
    For example:
    * South America and the Indian subcontinent breed most bovines who emit most methane.
    * China and the Middle East produce too much livestock methane in regard to their bovine headcounts.

    Emissions of from livestock digestion per world region:

  31. @food @ecology 🧵

    #Cattle husbandry emits #greenhouse gas.
    For example:
    * South America and the Indian subcontinent breed most bovines who emit most methane.
    * China and the Middle East produce too much livestock methane in regard to their bovine headcounts.

    Emissions of #methane from livestock digestion per world region:

    #Livestock #GreenHouseForcing #emissions #beef #bovines #dairy #GHG #climateChange #FAO #Copernicus #CH4 #climateBreakdown #China #India #Pakistan #MiddleEast #footprint

  32. Let's consider activities that force the Greenhouse Effect, thus @climate change. 🧵

    #Livestock digestion emits too much #methane:
    • People farm too many bovines in India, Pakistan, Brazil, United States, China;
    • People farm too many goats in India, Pakistan, China, Nigeria;
    • People farm too many pigs in China and the United States.

    #GreenHouseForcing #emissions #goats #beef #cattle #bovines #dairy #food #GHG #climateChange #FAO #EDGAR #Copernicus #CH4 #climateBreakdown #climateCollapse

  33. Let's consider activities that force the Greenhouse Effect, thus @climate change. 🧵

    #Livestock digestion emits too much #methane:
    • People farm too many bovines in India, Pakistan, Brazil, United States, China;
    • People farm too many goats in India, Pakistan, China, Nigeria;
    • People farm too many pigs in China and the United States.

    #GreenHouseForcing #emissions #goats #beef #cattle #bovines #dairy #food #GHG #climateChange #FAO #EDGAR #Copernicus #CH4 #climateBreakdown #climateCollapse

  34. Let's consider activities that force the Greenhouse Effect, thus @climate change. 🧵

    #Livestock digestion emits too much #methane:
    • People farm too many bovines in India, Pakistan, Brazil, United States, China;
    • People farm too many goats in India, Pakistan, China, Nigeria;
    • People farm too many pigs in China and the United States.

    #GreenHouseForcing #emissions #goats #beef #cattle #bovines #dairy #food #GHG #climateChange #FAO #EDGAR #Copernicus #CH4 #climateBreakdown #climateCollapse

  35. Let's consider activities that force the Greenhouse Effect, thus @climate change. 🧵

    digestion emits too much :
    • People farm too many bovines in India, Pakistan, Brazil, United States, China;
    • People farm too many goats in India, Pakistan, China, Nigeria;
    • People farm too many pigs in China and the United States.

  36. Let's consider activities that force the Greenhouse Effect, thus @climate change. 🧵

    #Livestock digestion emits too much #methane:
    • People farm too many bovines in India, Pakistan, Brazil, United States, China;
    • People farm too many goats in India, Pakistan, China, Nigeria;
    • People farm too many pigs in China and the United States.

    #GreenHouseForcing #emissions #goats #beef #cattle #bovines #dairy #food #GHG #climateChange #FAO #EDGAR #Copernicus #CH4 #climateBreakdown #climateCollapse