{"id":"W6964492153","doi":"10.25318/3810006701-eng","title":"Water use parameters in mineral extraction and thermal-electric power generation industries, by region","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extraction (chemistry); Water use; Electricity generation; Mineral water; Measure (data warehouse); Volume (thermodynamics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007265896,0.0001608521,0.000158884,0.00002776217,0.0001504347,0.00006124329,0.00007595218,0.0002364159,0.00006204721],"category_scores_gemma":[0.0001146378,0.00007340982,0.000009365848,0.0001173768,0.00003344249,0.0001240055,0.00003199665,0.0002074112,0.000003444912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001657458,"about_ca_system_score_gemma":0.00002453459,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1383956,"about_ca_topic_score_gemma":0.6535124,"domain_scores_codex":[0.9991282,0.00006741732,0.0001683972,0.0002585684,0.0001693657,0.0002080426],"domain_scores_gemma":[0.9995263,0.0002188817,0.00010526,0.00004182179,0.00006896092,0.00003879474],"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":[0.00001533421,0.00002910392,0.00108821,0.000006692434,0.00001206841,0.00001994397,0.00001223638,0.000004493354,0.003016201,0.000003718614,0.9945776,0.001214435],"study_design_scores_gemma":[0.0001574053,0.0002849859,0.2686775,0.00002812156,0.0000671637,0.00000922045,0.0001522887,0.00008600757,0.001262699,0.00001060588,0.7287818,0.0004821529],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.4195962,0.00003826059,0.000001340458,0.0002601832,0.0001952282,0.0001445233,0.5797603,0.000002812621,0.00000115493],"genre_scores_gemma":[0.1146852,0.0001561097,0.000009761596,0.0001193394,0.00002726499,0.000008273492,0.8846307,6.831843e-7,0.0003626426],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5151168,"threshold_uncertainty_score":0.8673419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986302002254317,"score_gpt":0.2162119970492302,"score_spread":0.1963489770266871,"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."}}