{"id":"W4412585422","doi":"10.21203/rs.3.rs-7187135/v1","title":"MetalliCan: A database on metal-related production activities and their socio-environmental impacts in Canada","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Haute école Spécialisée de Suisse Occidentale; Environment and Climate Change Canada; Government of Canada; École Polytechnique Fédérale de Lausanne; Polytechnique Montréal; Université du Québec à Montréal","keywords":"Production (economics); Database; Business; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003741482,0.001109879,0.0006994188,0.01191116,0.001166231,0.001875727,0.001329575,0.0005339032,0.015568],"category_scores_gemma":[0.002678141,0.0003972366,0.0007434085,0.03337074,0.0003331082,0.0006138784,0.0008314046,0.0003865144,0.004905913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01264388,"about_ca_system_score_gemma":0.03564956,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9879711,"about_ca_topic_score_gemma":0.9890544,"domain_scores_codex":[0.9991664,0.00002608858,0.00007033687,0.0001140523,0.0004434782,0.0001797198],"domain_scores_gemma":[0.9968946,0.0002982348,0.0002510307,0.0001535584,0.002078603,0.0003239343],"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":[0.0006098562,0.0001779895,0.2132632,0.003657044,0.0004735178,0.0005892872,0.001464095,0.01169273,0.002611543,0.007227701,0.6237214,0.1345116],"study_design_scores_gemma":[0.000062535,0.0000279978,0.2826994,0.0004399438,0.0002019402,0.0001505138,0.001253034,0.003250362,0.001611951,0.0007540007,0.7094584,0.00008984958],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01493989,0.0006335418,0.000483543,0.00007810882,0.000008309507,0.00005087841,0.9732202,0.0005040187,0.01008153],"genre_scores_gemma":[0.05097242,0.002350331,0.00236969,0.00005445556,0.000009662545,0.0001373792,0.9311601,0.0001423165,0.01280369],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.015568,"threshold_uncertainty_score":0.09173816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0347698276264882,"score_gpt":0.3068381524289754,"score_spread":0.2720683248024872,"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."}}