{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004280686,0.0005845226,0.0007577773,0.0016082,0.0006515802,0.0009340539,0.001034924,0.001266512,0.0018595],"category_scores_gemma":[0.001439138,0.0004585391,0.0006544642,0.0009414707,0.0006436525,0.0008642722,0.0006652169,0.0006329767,0.0006158999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005714927,"about_ca_system_score_gemma":0.0008645641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004007841,"about_ca_topic_score_gemma":0.004833434,"domain_scores_codex":[0.9997985,0.00003180188,0.000007206096,0.00006719322,0.00007104237,0.00002433661],"domain_scores_gemma":[0.9995781,0.000198918,0.00004355411,0.00004012768,0.0001242181,0.00001508537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002196422,0.000131143,0.002659583,0.0001081694,0.00006992785,0.0002128766,0.0001049202,0.5007495,0.04330091,0.009946263,0.001767406,0.4407297],"study_design_scores_gemma":[0.00001327498,0.00001674245,0.0002762658,0.000005711437,0.00001283151,0.0000422971,0.00002247798,0.9883742,0.005198272,0.005338192,0.000691861,0.000007872144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06096872,0.0002220309,0.9324963,0.0001538148,0.00004548517,0.0000630627,0.00005357367,0.001532442,0.004464563],"genre_scores_gemma":[0.4422017,0.0001309279,0.5534067,0.00008094179,0.00001471933,0.00008780068,0.0001147151,0.0002244514,0.003737964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004007841,"threshold_uncertainty_score":0.007969022,"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."}}