{"id":"W4412979330","doi":"10.1016/j.asoc.2025.113682","title":"Improving explainable AI in attributing hydrological responses to climate variabilities in snow-dominated watersheds","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; China Scholarship Council","keywords":"Snow; Environmental science; Climate change; Hydrology (agriculture); Physical geography; Computer science; Meteorology; Geology; Geography; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00222873,0.0002270841,0.0003556829,0.0002261837,0.0003632025,0.00004366053,0.0003185954,0.0001241995,0.00004261588],"category_scores_gemma":[0.0003346154,0.0002141193,0.00003554659,0.0007025844,0.0001632201,0.0001015697,0.00174883,0.0003227928,0.00009690873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000296526,"about_ca_system_score_gemma":0.000008875765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004354312,"about_ca_topic_score_gemma":0.0001463309,"domain_scores_codex":[0.9976801,0.0001381139,0.0004800853,0.0006389135,0.0001377704,0.0009250578],"domain_scores_gemma":[0.9989777,0.0006766802,0.00006331175,0.0002286108,0.000006055105,0.00004765876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007378233,0.0002311549,0.8479031,0.0001235801,0.00003135555,0.00009977251,0.005876541,0.09029994,0.01654927,0.004633847,0.0002609862,0.03325259],"study_design_scores_gemma":[0.005283433,0.0003113178,0.790222,0.0003818029,0.00006680306,0.000006429261,0.006711091,0.1571952,0.01651813,0.01723159,0.004367708,0.001704495],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786583,0.00001151031,0.009158342,0.00137073,0.0001077405,0.000518056,8.300659e-7,0.0001207191,0.0100538],"genre_scores_gemma":[0.9953237,0.000002738997,0.002178495,0.002221659,0.00001304592,0.000063142,0.000004700627,0.00001038279,0.0001821101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06689526,"threshold_uncertainty_score":0.8731533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007907984167516298,"score_gpt":0.2379568011218005,"score_spread":0.2300488169542842,"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."}}