{"id":"W6939001582","doi":"10.6068/dp15e7376453a29","title":"Trend 2013 - 2014. Energy Information Administration. International Energy Statistics: Coal | Country: Canada | Category: Production | Series: Subbituminous Production | Units: Metric Tons of Oil Equivalent, 2013-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 004-015-002.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Production (economics); Tonne; Coal; Administration (probate law); Energy policy; Energy (signal processing); Agency (philosophy); Environmental impact of the energy industry","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":[0.001562362,0.001991078,0.002288967,0.006764591,0.001888699,0.00467818,0.00380632,0.001252435,0.0709364],"category_scores_gemma":[0.01318759,0.001386123,0.001513698,0.03438767,0.0005929673,0.002901981,0.001892771,0.003133076,0.06427641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01962049,"about_ca_system_score_gemma":0.04531618,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9332367,"about_ca_topic_score_gemma":0.9193489,"domain_scores_codex":[0.9973899,0.0001594913,0.0002716082,0.0003602986,0.001293481,0.0005253581],"domain_scores_gemma":[0.9812134,0.0008389213,0.0006730374,0.0009226039,0.01558052,0.0007715223],"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":[0.00001963725,0.000006601171,0.000514833,0.0001768331,0.00001506798,0.000004080375,0.000009374697,0.0001068597,0.00001140473,0.0003229206,0.9976883,0.001124047],"study_design_scores_gemma":[0.0000886578,0.000006959344,0.01029475,0.0004382238,0.00002875317,0.00001189314,0.0001930921,0.0003402557,0.0002108571,0.0006788627,0.9876638,0.00004396206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000304616,0.00002300397,0.000017109,0.00005393964,0.00001895318,0.000007121176,0.999253,0.00005236851,0.0005440242],"genre_scores_gemma":[0.0002358675,0.00008056398,0.0001307487,0.00003726102,0.000007914037,0.00004188582,0.9982195,0.00005184772,0.001194337],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0709364,"threshold_uncertainty_score":0.2373059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03025786429937632,"score_gpt":0.2656184035695208,"score_spread":0.2353605392701445,"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."}}