{"id":"W2038884454","doi":"10.1016/j.cageo.2015.04.008","title":"A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering","year":2015,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Thin section; Segmentation; Cluster analysis; Geology; Petrography; Hue; Mineralogy; Thin film; Mineral; Mars Exploration Program; Artificial intelligence; Computer science; Pattern recognition (psychology); Materials science; Physics","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":[0.0004942783,0.001132407,0.001233593,0.003244048,0.001075521,0.001221899,0.002861556,0.001153349,0.00294951],"category_scores_gemma":[0.0008725941,0.0008094893,0.001406697,0.002520193,0.0006041304,0.001684997,0.001166813,0.0009813458,0.001333018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008224294,"about_ca_system_score_gemma":0.00171839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01261128,"about_ca_topic_score_gemma":0.01920887,"domain_scores_codex":[0.9992484,0.00005151298,0.00003674634,0.0002634808,0.00033455,0.00006524221],"domain_scores_gemma":[0.9994026,0.00008861817,0.00003862086,0.00008968476,0.0003388074,0.00004163563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001884297,0.0001927779,0.001729509,0.0003211364,0.0001987157,0.0001252447,0.000190669,0.0449968,0.1154746,0.006367947,0.006504409,0.82371],"study_design_scores_gemma":[0.00003284514,0.00005685624,0.001688319,0.00001238532,0.00009790161,0.0002742767,0.00005538456,0.9569131,0.03032795,0.002971875,0.007490752,0.00007843094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00481722,0.000150824,0.9925707,0.00005126994,0.00005883613,0.00006248308,0.0000610399,0.001386694,0.000840859],"genre_scores_gemma":[0.04095268,0.0001704293,0.9558606,0.00006204168,0.00004514037,0.00009715438,0.0002601278,0.0003024698,0.002249324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01261128,"threshold_uncertainty_score":0.02507573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06080255484459258,"score_gpt":0.3160816534234656,"score_spread":0.255279098578873,"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."}}