{"id":"W4395005073","doi":"10.1080/02508060.2024.2321823","title":"Water security through community-directed monitoring in the Canadian Columbia Basin: democratizing watershed data","year":2024,"lang":"en","type":"article","venue":"Water International","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Insight Electronics (Canada); Lakes Environmental (Canada)","funders":"","keywords":"Watershed; Structural basin; Water resource management; Environmental science; Water security; Hydrology (agriculture); Geography; Water resources; Geology; Ecology; Computer science","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":[0.008586942,0.0003444148,0.0002711425,0.003953381,0.002801437,0.003520905,0.001257148,0.0006430023,0.001782493],"category_scores_gemma":[0.02199281,0.0002829927,0.0001847826,0.007830416,0.001653134,0.002458173,0.003415368,0.001195806,0.0003114767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01258168,"about_ca_system_score_gemma":0.03412473,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9215245,"about_ca_topic_score_gemma":0.9619363,"domain_scores_codex":[0.9949644,0.001868528,0.0002333209,0.0007320007,0.001632954,0.0005687989],"domain_scores_gemma":[0.9878733,0.002948026,0.000930887,0.003050118,0.004329867,0.0008678873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002446358,0.0002759272,0.2567721,0.0002401375,0.0001761132,0.0002860005,0.003045623,0.0703047,0.00494841,0.04545858,0.09067901,0.5275688],"study_design_scores_gemma":[0.0001814282,0.00006506325,0.2649855,0.0006214373,0.0001302487,0.0001304673,0.01172419,0.4518411,0.007784535,0.04995757,0.2123402,0.0002382897],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7069043,0.001769655,0.1027328,0.04124815,0.0002349738,0.001910808,0.04071239,0.001903574,0.1025833],"genre_scores_gemma":[0.8886414,0.0008305646,0.09204298,0.0006283683,0.00005309594,0.0003972463,0.01214347,0.0001120946,0.005150848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07847548,"threshold_uncertainty_score":0.1578752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04097542826536543,"score_gpt":0.2754463882930555,"score_spread":0.23447096002769,"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."}}