{"id":"W4407576008","doi":"10.48550/arxiv.2502.09356","title":"AI4SNOW SnowGalileo Datasets and Model Checkpoints","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nuclear Safety and Security Commission; National Aeronautics and Space Administration; European Space Agency; Canadian Institute for Advanced Research; Nvidia","keywords":"Galileo (satellite navigation); Remote sensing; Computer science; Artificial intelligence; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001479426,0.002836271,0.001136629,0.001732524,0.0008531811,0.001373476,0.004165091,0.002090659,0.02630948],"category_scores_gemma":[0.003402092,0.0007356324,0.002089263,0.00231899,0.0005999806,0.001625812,0.001588423,0.002392862,0.03243637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001366178,"about_ca_system_score_gemma":0.001689686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03211334,"about_ca_topic_score_gemma":0.05344952,"domain_scores_codex":[0.9992819,0.0001022698,0.00006337245,0.0002450285,0.0002193826,0.00008799417],"domain_scores_gemma":[0.9988406,0.0002161913,0.00006019366,0.0004179683,0.0003729275,0.0000920182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002334679,0.0001961132,0.0028846,0.0005354082,0.0001057095,0.000100397,0.00005007699,0.01770761,0.0011408,0.0007307498,0.9610617,0.01525336],"study_design_scores_gemma":[0.001177904,0.0002554721,0.01700092,0.0003766627,0.0001409847,0.0002273482,0.0002931378,0.1355779,0.007845923,0.007401892,0.8294836,0.0002181968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005592066,0.0002559777,0.00284457,0.0002997135,0.0002123972,0.000181683,0.9695299,0.01776678,0.003316936],"genre_scores_gemma":[0.003787438,0.0000532605,0.003289161,0.00004886861,0.0000145859,0.0001669284,0.9912374,0.0005575811,0.0008448504],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03211334,"threshold_uncertainty_score":0.08801401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07124793144128899,"score_gpt":0.345184657486942,"score_spread":0.273936726045653,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}