{"id":"W4385932451","doi":"10.1126/science.abn5962","title":"Extracting resources from abandoned mines","year":2023,"lang":"en","type":"article","venue":"Science","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mining engineering; Environmental science; Waste management; Geology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0004090875,0.0003847184,0.0004209582,0.001454067,0.001172185,0.001603982,0.0006352213,0.0007695272,0.003876293],"category_scores_gemma":[0.0007306011,0.0002756137,0.0003250278,0.001221429,0.00062912,0.001136672,0.001102864,0.0006149289,0.002122356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008688002,"about_ca_system_score_gemma":0.001645298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004934444,"about_ca_topic_score_gemma":0.01885347,"domain_scores_codex":[0.9997237,0.00002391446,0.00001822084,0.00004470972,0.000117897,0.00007147686],"domain_scores_gemma":[0.9997604,0.00003265321,0.00003528759,0.00003166799,0.0001053358,0.00003458923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006034623,0.0001764244,0.02870331,0.001769469,0.0001012821,0.002955741,0.001307195,0.006570205,0.4764726,0.01020683,0.01028716,0.4608464],"study_design_scores_gemma":[0.0001149153,0.0007038553,0.04919785,0.001441536,0.0001422364,0.002807427,0.005679702,0.01324825,0.4529816,0.0325521,0.4410141,0.0001164414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8861711,0.009003721,0.04150848,0.004648469,0.0003736192,0.0002355901,0.001875105,0.0004835181,0.05570034],"genre_scores_gemma":[0.9167543,0.009986197,0.03635792,0.0008618612,0.00007549202,0.00009158715,0.001251549,0.0001436079,0.03447758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004934444,"threshold_uncertainty_score":0.01296753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750006760323763,"score_gpt":0.2460129917612397,"score_spread":0.228512924158002,"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."}}