{"id":"W6920377109","doi":"10.6068/dp14ba7b5641c15","title":"Trend 1978 - 2011. Statistics Canada. CANSIM: Environment - Natural Resources | Country: Canada | Table: Proven and probable zinc reserves | Variable: Additions, proven and probable zinc reserves | Units: Tonnes x 1,000, 1978-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-086.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Natural resource; Census; Summary statistics; Official statistics; Mineral resource classification","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","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001871865,0.001990697,0.002199644,0.0003181811,0.0007535351,0.00103272,0.004168358,0.0009310831,0.009114992],"category_scores_gemma":[0.0003977123,0.001853745,5.130227e-7,0.0004092345,0.001157488,0.0013538,0.003286238,0.00193604,0.0002924951],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009132827,"about_ca_system_score_gemma":0.01486865,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9996574,"about_ca_topic_score_gemma":0.9996391,"domain_scores_codex":[0.9879298,0.001392158,0.001885669,0.003458136,0.003020442,0.002313822],"domain_scores_gemma":[0.9891315,0.001209319,0.001829976,0.006124545,0.000193722,0.001510966],"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":[0.0005007209,0.0002426978,0.00006879089,0.002305198,0.0009052143,0.0008434645,0.000008270797,0.00003565629,0.00001857788,0.001075277,0.9939368,0.00005926671],"study_design_scores_gemma":[0.001800133,0.0003033757,0.00001682233,0.0002398957,0.001001439,0.0004263467,0.0003359642,0.001415861,2.313476e-7,0.000003650592,0.9922228,0.002233515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000001958405,0.01541516,7.357912e-7,0.00002360189,0.0005535984,0.003540131,0.9746156,0.0002473136,0.005601892],"genre_scores_gemma":[0.000005309771,0.001883272,0.001136057,0.0001491924,0.0005047124,0.0002931267,0.9566231,0.0007161916,0.03868898],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03308709,"threshold_uncertainty_score":0.9992836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120458755602701,"score_gpt":0.2273987561173841,"score_spread":0.2061941685613571,"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."}}