{"id":"W4312610707","doi":"10.1130/abs/2022am-378325","title":"CAN CRITICAL METAL SUPPLY PROBLEMS BE SOLVED USING EXISTING BUT HIDDEN MATERIALS FLOWS? TELLURIUM AND THE USA-CANADIAN MINING VALUE CHAIN","year":2022,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Tellurium; Supply chain; Value (mathematics); Computer science; Chain (unit); Materials science; Business; Metallurgy; Machine learning; Physics","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":[0.001149266,0.0003260912,0.0002018564,0.001075062,0.004291844,0.006358122,0.001071682,0.001261606,0.01100186],"category_scores_gemma":[0.006459354,0.0002323472,0.0003960329,0.002231011,0.004213973,0.004738388,0.001473989,0.00146694,0.0004179713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03833428,"about_ca_system_score_gemma":0.07453794,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.95562,"about_ca_topic_score_gemma":0.9794418,"domain_scores_codex":[0.9991146,0.00009917789,0.00001889076,0.00007244234,0.0002390503,0.0004557773],"domain_scores_gemma":[0.9980178,0.0004143184,0.0001995151,0.00008248456,0.001060011,0.0002257896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002937106,0.0001741288,0.07444175,0.0003915945,0.0001129694,0.001270796,0.005000419,0.04126283,0.002957771,0.6628679,0.03690227,0.1743239],"study_design_scores_gemma":[0.0001220685,0.0001488753,0.1029902,0.0009007725,0.0002205185,0.0003260878,0.06962554,0.04898385,0.005763311,0.4756018,0.2950867,0.0002302964],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5039923,0.003246387,0.01815454,0.104838,0.000248823,0.000147428,0.0009225346,0.0001687848,0.3682813],"genre_scores_gemma":[0.9725372,0.002554239,0.003734971,0.0007838032,0.00002380815,0.00001240949,0.000129397,0.00002156547,0.02020267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04438001,"threshold_uncertainty_score":0.278136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03954062066852727,"score_gpt":0.2549582449497712,"score_spread":0.2154176242812439,"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."}}