{"id":"W4417538516","doi":"10.48550/arxiv.2506.16531","title":"How Hard Is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Domain adaptation; Snow; Domain (mathematical analysis); Sequence (biology); Noise (video); Object detection; Object (grammar); Point (geometry)","routes":{"ca_aff":false,"ca_fund":false,"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":[0.0008745681,0.001393495,0.0007267091,0.001344948,0.0009502002,0.001139215,0.00164853,0.001801469,0.002356964],"category_scores_gemma":[0.003316942,0.0003446417,0.001212875,0.001376683,0.0008621016,0.0008107719,0.001110076,0.001516682,0.002248553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001508972,"about_ca_system_score_gemma":0.001611246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08728717,"about_ca_topic_score_gemma":0.2247326,"domain_scores_codex":[0.99897,0.0001323473,0.00005641537,0.000402634,0.0002870019,0.00015159],"domain_scores_gemma":[0.9985642,0.0002977394,0.00008740333,0.000499452,0.0004205735,0.0001307141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001742701,0.001487246,0.08901284,0.001658117,0.0008240136,0.00134646,0.0006503113,0.1203663,0.02353422,0.002364307,0.518229,0.2387844],"study_design_scores_gemma":[0.0004406266,0.0006092413,0.2392047,0.0003411839,0.0002258359,0.001962765,0.0018802,0.4645239,0.0309101,0.00662234,0.2528648,0.0004143068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5437081,0.002247847,0.03705014,0.001736743,0.001147178,0.001136279,0.3807034,0.01469064,0.01757965],"genre_scores_gemma":[0.3529146,0.0003831176,0.04729759,0.0005769363,0.0001004201,0.0003956675,0.5930979,0.0005810186,0.004652624],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.08728717,"threshold_uncertainty_score":0.1735582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.077045986734562,"score_gpt":0.2940337026000955,"score_spread":0.2169877158655335,"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."}}