{"id":"W1495472409","doi":"10.4236/jmmce.2011.1012085","title":"Development and Calibration of a Quantitative, Automated Mineralogical Assessment Method Based on SEM-EDS and Image Analysis: Application for Fine Tailings","year":2011,"lang":"en","type":"article","venue":"Journal of Minerals and Materials Characterization and Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agnico Eagle (Canada); Université du Québec en Abitibi-Témiscamingue; Centre Technologique des Résidus Industriels; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tailings; Calibration; Environmental science; Metallurgy; Materials science; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002354199,0.001026808,0.0006508485,0.002255231,0.0004090593,0.0008369189,0.001488608,0.001534289,0.001255765],"category_scores_gemma":[0.002010908,0.0007653445,0.000551722,0.001269113,0.0006718782,0.0009393467,0.0009376009,0.001002922,0.0009820242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005544936,"about_ca_system_score_gemma":0.0008478755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134442,"about_ca_topic_score_gemma":0.003238348,"domain_scores_codex":[0.9978697,0.0001799775,0.000118615,0.0004358469,0.001333773,0.00006207523],"domain_scores_gemma":[0.9980709,0.0003302618,0.0001928431,0.0002406249,0.001112077,0.00005325867],"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":[0.00007051402,0.00007692863,0.003326831,0.0003956307,0.00003478034,0.00006575401,0.0001444776,0.001806065,0.8979336,0.000574992,0.0005371125,0.09503326],"study_design_scores_gemma":[0.00001654352,0.000238101,0.009002629,0.00003621931,0.00005695164,0.0004848679,0.000132827,0.04210992,0.9359254,0.0005837674,0.01133572,0.00007707269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05701207,0.0007735745,0.9362959,0.00008845533,0.00009717068,0.0003800056,0.0004213353,0.003215242,0.001716275],"genre_scores_gemma":[0.1364113,0.0005858585,0.858713,0.00007984955,0.0000217622,0.0003507895,0.0003285349,0.0003208039,0.003188169],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002354199,"threshold_uncertainty_score":0.0124504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160922972795495,"score_gpt":0.2711249975607471,"score_spread":0.2495157678327921,"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."}}