{"id":"W6888855479","doi":"10.25318/3810007201-eng","title":"Intake water treatment in mineral extraction and thermal-electric power generation industries, by type of treatment and region","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extraction (chemistry); Water treatment; Mineral water; Electricity generation; Water extraction; Measure (data warehouse); Mineral","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009981005,0.000409392,0.0004204972,0.0003134109,0.00006329317,0.00005171313,0.00005790772,0.0002751071,0.00006538851],"category_scores_gemma":[0.00008648221,0.0003326548,0.00001131497,0.0002340556,0.00004062233,0.0001586333,0.00001590501,0.0001512319,0.000004625569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002878515,"about_ca_system_score_gemma":0.0006038167,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2887668,"about_ca_topic_score_gemma":0.5203227,"domain_scores_codex":[0.9983811,0.000137366,0.0004691248,0.0004498803,0.0002780304,0.0002845229],"domain_scores_gemma":[0.9989666,0.0001405458,0.000342168,0.0002767881,0.0001829602,0.0000909095],"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":[0.00009657761,0.0001606128,0.0001585945,0.00004047439,0.00006777518,0.00002653846,0.0001165941,0.00003063294,0.03207718,0.00001782037,0.9632909,0.003916246],"study_design_scores_gemma":[0.002311877,0.003178713,0.01377081,0.000162668,0.0006848126,0.0000679618,0.000252861,0.001219903,0.03163238,0.00001579549,0.9455606,0.001141568],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.183561,0.0003598394,0.00001795209,0.00003808344,0.0002247929,0.0006486087,0.8151391,0.000005427329,0.000005224075],"genre_scores_gemma":[0.115871,0.0005872934,0.00003767724,0.00001054751,0.00003923364,0.00005199244,0.8826923,0.00004405671,0.0006659198],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.231556,"threshold_uncertainty_score":0.9999126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02014219725683945,"score_gpt":0.2784110401049039,"score_spread":0.2582688428480645,"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."}}