{"id":"W2530108535","doi":"10.1002/hyp.10880","title":"CANOPEX: A Canadian hydrometeorological watershed database","year":2016,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Hydrometeorology; Watershed; Streamflow; Database; Hydrological modelling; Environmental science; Precipitation; Variety (cybernetics); Climate change; Robustness (evolution); Meteorology; Climatology; Drainage basin; Computer science; Hydrology (agriculture); Environmental resource management; Geography; Cartography; Geology; Machine learning","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":[0.0009804603,0.0009444787,0.0006996231,0.00781047,0.001251741,0.002773812,0.002383698,0.0007181512,0.01787345],"category_scores_gemma":[0.006614011,0.0004297532,0.0005254967,0.01915662,0.0004950546,0.001551604,0.001657784,0.0008031368,0.00663793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009685731,"about_ca_system_score_gemma":0.02532774,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9112837,"about_ca_topic_score_gemma":0.912568,"domain_scores_codex":[0.99892,0.00006945138,0.0001366963,0.0002403211,0.0004810791,0.0001525466],"domain_scores_gemma":[0.9953905,0.0004572228,0.0003258516,0.0007951038,0.002445654,0.0005856312],"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":[0.0002311038,0.00005909526,0.01732759,0.0007273692,0.0001035154,0.0002169541,0.0003107022,0.009940071,0.0008961891,0.01162847,0.9130518,0.04550708],"study_design_scores_gemma":[0.0001876616,0.00001041692,0.0442997,0.000245019,0.00004904044,0.00008329929,0.0004078885,0.01505607,0.001343063,0.004883836,0.9332781,0.0001560016],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003259964,0.0001267706,0.002180318,0.0001253989,0.00001310505,0.0001168762,0.9882368,0.001942718,0.003997983],"genre_scores_gemma":[0.01240107,0.0002431566,0.007003196,0.00004999448,0.000005650891,0.0002963964,0.9782549,0.0003193551,0.001426328],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08871627,"threshold_uncertainty_score":0.1784774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01851742126247816,"score_gpt":0.2174510324947891,"score_spread":0.1989336112323109,"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."}}