{"id":"W2148554518","doi":"10.1007/pl00013273","title":"Automatic mineral identification using genetic programming","year":2001,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Thresholding; Artificial intelligence; Computer science; Mineral resource classification; Identification (biology); Mineral processing; Image processing; Suite; Mineral; Computer vision; Genetic algorithm; Computation; Image (mathematics); Pattern recognition (psychology); Geology; Machine learning; Algorithm; Geography; Materials science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001298884,0.0001201592,0.000100034,0.0001138226,0.0004878629,0.0002005205,0.0003440554,0.00003974004,0.00002142561],"category_scores_gemma":[0.000006160628,0.0001092398,0.00003876689,0.0006694121,0.00005446919,0.0002580519,0.0001347199,0.00007889976,0.00004587676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002564364,"about_ca_system_score_gemma":0.00002355495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004414086,"about_ca_topic_score_gemma":0.000004756827,"domain_scores_codex":[0.9989748,0.00002654386,0.0002853074,0.0003762785,0.0001606954,0.0001763785],"domain_scores_gemma":[0.9991968,0.00003496984,0.0001038056,0.0004937524,0.00006208206,0.0001085638],"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":[5.294002e-7,0.0001962023,0.001285187,0.00001246263,0.000006779032,0.000001197391,0.00007938252,0.0002974193,0.004247281,0.05152727,0.0001414257,0.9422048],"study_design_scores_gemma":[0.0001518997,0.00001779465,0.0297735,0.000007844462,0.00001014114,0.00009500262,0.00001428747,0.9107859,0.00003560652,0.004854471,0.05410977,0.0001438492],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05565333,0.0003993083,0.9417441,0.001239654,0.00002824597,0.000482532,0.000003919366,0.000241041,0.0002078813],"genre_scores_gemma":[0.6465726,0.00008996385,0.352412,0.0001383299,0.000104388,0.0003619102,0.00002521729,0.00001194304,0.0002836774],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.942061,"threshold_uncertainty_score":0.4454668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332112028148058,"score_gpt":0.2931154093691397,"score_spread":0.2797942890876591,"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."}}