{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001128744,0.0002691769,0.0004185722,0.0002255043,0.0007665788,0.0001343075,0.0005453727,0.0003668999,0.00004853397],"category_scores_gemma":[0.0003562056,0.0002711919,0.0001126879,0.0002682985,0.0003964897,0.0002785867,0.001344433,0.0004526958,0.0001265068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008369874,"about_ca_system_score_gemma":0.0003377596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003833824,"about_ca_topic_score_gemma":0.002742235,"domain_scores_codex":[0.9980422,0.0001191973,0.0005054454,0.0004440076,0.0004727895,0.0004163636],"domain_scores_gemma":[0.9986616,0.00008802511,0.0003051048,0.0005847369,0.0002316796,0.0001288019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002125198,0.0000833791,0.8055746,0.0009852325,0.0005008815,0.000008449282,0.06793898,0.0005020331,0.00001107901,0.02560685,0.09691435,0.001852934],"study_design_scores_gemma":[0.001667627,0.00004439953,0.326833,0.002030036,0.0004793468,0.000004277348,0.03385779,0.00504161,0.0001371976,0.0238971,0.6032351,0.002772532],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6557937,0.002143991,0.001623452,0.008176396,0.004209183,0.002258191,0.00295373,0.0006313042,0.32221],"genre_scores_gemma":[0.9885167,0.001071984,0.0004699755,0.0005939023,0.0002358694,0.0001470215,0.0003076743,0.00001089201,0.008645923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5063207,"threshold_uncertainty_score":0.999974,"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."}}