{"id":"W4413018704","doi":"10.1109/iv64158.2025.11097651","title":"How Hard is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+","year":2025,"lang":"en","type":"article","venue":"","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Snow; Computer science; Adaptation (eye); Domain (mathematical analysis); Domain adaptation; Meteorology; Artificial intelligence; Geography; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009409336,0.001922989,0.0009807667,0.00190828,0.001051236,0.001185776,0.002165314,0.002320252,0.00383523],"category_scores_gemma":[0.003339161,0.0004386929,0.001725474,0.002292123,0.0007686401,0.0009738759,0.001297222,0.001780657,0.004606079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001864846,"about_ca_system_score_gemma":0.001536131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08418009,"about_ca_topic_score_gemma":0.1887535,"domain_scores_codex":[0.9985479,0.0001905632,0.00009543177,0.0005668699,0.0004093612,0.0001898133],"domain_scores_gemma":[0.9978468,0.0004683381,0.00012193,0.0006619747,0.000654255,0.0002467361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009002816,0.00093904,0.04038387,0.001185228,0.0004570662,0.0007898899,0.0003430721,0.04004881,0.007427501,0.001023612,0.8135482,0.09295344],"study_design_scores_gemma":[0.0006093244,0.0005014301,0.2400106,0.000324298,0.0002591633,0.001617637,0.001788495,0.1953988,0.01937837,0.004048028,0.5356079,0.0004559686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1594743,0.001064259,0.01014689,0.001079312,0.00073298,0.000798636,0.8050638,0.01006485,0.01157489],"genre_scores_gemma":[0.06809835,0.000152305,0.01313446,0.0003008785,0.00005489006,0.0003191789,0.9150985,0.000293884,0.002547465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08418009,"threshold_uncertainty_score":0.1673802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903448695918504,"score_gpt":0.2810107457626512,"score_spread":0.2619762588034661,"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."}}