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#glacial — Public Fediverse posts

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

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  1. Compiling my Rust server. I had one changed file. Rust's compiler is probably the slowest compiler that I have ever seen and I have seen *many* over the decades. Disregarding everything extra it does that other compilers don't it's still glacially slow, probably a magnitude slower than compiling C++ on this system would be.

    #rust #glacial

  2. Compiling my Rust server. I had one changed file. Rust's compiler is probably the slowest compiler that I have ever seen and I have seen *many* over the decades. Disregarding everything extra it does that other compilers don't it's still glacially slow, probably a magnitude slower than compiling C++ on this system would be.

    #rust #glacial

  3. Compiling my Rust server. I had one changed file. Rust's compiler is probably the slowest compiler that I have ever seen and I have seen *many* over the decades. Disregarding everything extra it does that other compilers don't it's still glacially slow, probably a magnitude slower than compiling C++ on this system would be.

    #rust #glacial

  4. Compiling my Rust server. I had one changed file. Rust's compiler is probably the slowest compiler that I have ever seen and I have seen *many* over the decades. Disregarding everything extra it does that other compilers don't it's still glacially slow, probably a magnitude slower than compiling C++ on this system would be.

    #rust #glacial

  5. Compiling my Rust server. I had one changed file. Rust's compiler is probably the slowest compiler that I have ever seen and I have seen *many* over the decades. Disregarding everything extra it does that other compilers don't it's still glacially slow, probably a magnitude slower than compiling C++ on this system would be.

    #rust #glacial

  6. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  7. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  8. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  9. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  10. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”

  11. #Geologically, the #Germancapital, #Berlin, is a remnant of a region situated near a #glacial #terminalmoraine. The #city's natural chains of #lakes date back to this era. However, even restored #artificial #bodiesofwater—such as the #Flughafensee, pictured below—adapt to the typical #wetlandecology characterized by thick layers of #sand.

    © #StefanFWirth

    I rely deeply on your financial donation: ko-fi.com/sfwirth

    Photos
    © S.F. Wirth, March 2026 (edited May)

  12. #Geologically, the #Germancapital, #Berlin, is a remnant of a region situated near a #glacial #terminalmoraine. The #city's natural chains of #lakes date back to this era. However, even restored #artificial #bodiesofwater—such as the #Flughafensee, pictured below—adapt to the typical #wetlandecology characterized by thick layers of #sand.

    © #StefanFWirth

    I rely deeply on your financial donation: ko-fi.com/sfwirth

    Photos
    © S.F. Wirth, March 2026 (edited May)

  13. #Geologically, the #Germancapital, #Berlin, is a remnant of a region situated near a #glacial #terminalmoraine. The #city's natural chains of #lakes date back to this era. However, even restored #artificial #bodiesofwater—such as the #Flughafensee, pictured below—adapt to the typical #wetlandecology characterized by thick layers of #sand.

    © #StefanFWirth

    I rely deeply on your financial donation: ko-fi.com/sfwirth

    Photos
    © S.F. Wirth, March 2026 (edited May)

  14. #Geologically, the #Germancapital, #Berlin, is a remnant of a region situated near a #glacial #terminalmoraine. The #city's natural chains of #lakes date back to this era. However, even restored #artificial #bodiesofwater—such as the #Flughafensee, pictured below—adapt to the typical #wetlandecology characterized by thick layers of #sand.

    © #StefanFWirth

    I rely deeply on your financial donation: ko-fi.com/sfwirth

    Photos
    © S.F. Wirth, March 2026 (edited May)

  15. #Geologically, the #Germancapital, #Berlin, is a remnant of a region situated near a #glacial #terminalmoraine. The #city's natural chains of #lakes date back to this era. However, even restored #artificial #bodiesofwater—such as the #Flughafensee, pictured below—adapt to the typical #wetlandecology characterized by thick layers of #sand.

    © #StefanFWirth

    I rely deeply on your financial donation: ko-fi.com/sfwirth

    Photos
    © S.F. Wirth, March 2026 (edited May)

  16. Compiling Rust code is so sloooow.... I don't notice it that much on my M1 Pro Max which is still a somewhat decent machine, but on a bog standard VPS it takes about 10x longer and it's hurting the morale of my one man development team.

    #rust #glacial

  17. Compiling Rust code is so sloooow.... I don't notice it that much on my M1 Pro Max which is still a somewhat decent machine, but on a bog standard VPS it takes about 10x longer and it's hurting the morale of my one man development team.

    #rust #glacial

  18. Compiling Rust code is so sloooow.... I don't notice it that much on my M1 Pro Max which is still a somewhat decent machine, but on a bog standard VPS it takes about 10x longer and it's hurting the morale of my one man development team.

    #rust #glacial

  19. Compiling Rust code is so sloooow.... I don't notice it that much on my M1 Pro Max which is still a somewhat decent machine, but on a bog standard VPS it takes about 10x longer and it's hurting the morale of my one man development team.

    #rust #glacial

  20. Compiling Rust code is so sloooow.... I don't notice it that much on my M1 Pro Max which is still a somewhat decent machine, but on a bog standard VPS it takes about 10x longer and it's hurting the morale of my one man development team.

    #rust #glacial