{"id":"W4322504372","doi":"10.3390/data8030048","title":"Reconstructed River Water Temperature Dataset for Western Canada 1980–2018","year":2023,"lang":"en","type":"article","venue":"Data","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Baseline (sea); Climate change; Water quality; Air temperature; Ecosystem; Calibration; Aquatic ecosystem; Climatology; Hydrology (agriculture); Geology; Ecology; Oceanography; Statistics","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":[0.0002771737,0.0007770672,0.0004227172,0.001655233,0.001038593,0.000754185,0.001444333,0.0004078675,0.003921587],"category_scores_gemma":[0.001220875,0.0003227607,0.0006143689,0.004900946,0.0003516571,0.0003219674,0.0005128742,0.0007154291,0.001873646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0107648,"about_ca_system_score_gemma":0.02225754,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9843308,"about_ca_topic_score_gemma":0.9908559,"domain_scores_codex":[0.9996413,0.00001569777,0.00002616324,0.00008861559,0.0001404642,0.00008767888],"domain_scores_gemma":[0.9985178,0.00003428734,0.00006958834,0.000109204,0.001171365,0.00009764088],"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":[0.0004315249,0.0001766951,0.1718795,0.0006967625,0.0005614867,0.0004502903,0.0004137404,0.06855541,0.002523532,0.002834717,0.7052217,0.04625473],"study_design_scores_gemma":[0.000329022,0.00003916632,0.421304,0.000360024,0.0001956588,0.0002033815,0.0008592638,0.08222611,0.004791127,0.001268934,0.4881977,0.000225449],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04173251,0.0001915932,0.001013704,0.0001471253,0.00003344778,0.00004743274,0.9533588,0.0006767954,0.002798529],"genre_scores_gemma":[0.05182172,0.0001875492,0.002332615,0.00005072206,0.000009096272,0.00007942998,0.9435479,0.00006870113,0.001902326],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01566917,"threshold_uncertainty_score":0.0781045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02553500704588468,"score_gpt":0.2370561066592256,"score_spread":0.2115210996133409,"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."}}