{"id":"W6888931523","doi":"10.25318/3810007001-eng","title":"Water intake in mineral extraction and thermal-electric power generation industries, by source, by region and by industry","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extraction (chemistry); Electricity generation; Mineral water; Water intake; Water extraction; Volume (thermodynamics); Measure (data warehouse)","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":[0.0005590775,0.001475373,0.001387052,0.003805582,0.0006625235,0.001740031,0.002167555,0.001077991,0.04445435],"category_scores_gemma":[0.006664978,0.0007788438,0.001500365,0.0161048,0.0004175511,0.001052418,0.001171007,0.001642104,0.02615133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004921815,"about_ca_system_score_gemma":0.009201833,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7142194,"about_ca_topic_score_gemma":0.7613279,"domain_scores_codex":[0.9991411,0.00006911289,0.0001438439,0.0002232198,0.000262923,0.000159784],"domain_scores_gemma":[0.9962646,0.0005321811,0.0004333489,0.0003122239,0.002187776,0.000269812],"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":[0.00004875768,0.000009596087,0.00222346,0.0007295113,0.00005240528,0.00001061534,0.00001499346,0.0002604722,0.00003033624,0.0003482935,0.9947726,0.001498992],"study_design_scores_gemma":[0.0005525078,0.00002039044,0.05706794,0.001209466,0.0001122136,0.00007625898,0.0001962534,0.0008521421,0.0003087976,0.0008864662,0.9386407,0.00007692983],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000787079,0.00003901868,0.000013862,0.00003044373,0.000008327577,0.000003094848,0.9995388,0.00002859891,0.0002592129],"genre_scores_gemma":[0.0009487376,0.0001498814,0.0001078533,0.00004860777,0.000007455118,0.00003896804,0.9977942,0.00002914724,0.0008752429],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2857806,"threshold_uncertainty_score":0.5749272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009075101419536678,"score_gpt":0.2437305691235151,"score_spread":0.2346554677039784,"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."}}