{"id":"W2179527983","doi":"10.1016/j.minpro.2015.09.015","title":"Rock lithological classification using multi-scale Gabor features from sub-images, and voting with rock contour information","year":2015,"lang":"en","type":"article","venue":"International Journal of Mineral Processing","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Fondo de Fomento al Desarrollo Científico y Tecnológico; Fondo Nacional de Desarrollo Científico y Tecnológico; Comisión Nacional de Investigación Científica y Tecnológica; Centro Avanzado de Tecnología para la Minería; Université Laval","keywords":"Pixel; Geology; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Scale (ratio); Feature (linguistics); Texture (cosmology); Computer science; Feature extraction; Remote sensing; Computer vision; Image (mathematics); Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002599602,0.0001862175,0.0002253194,0.0002108728,0.0000786602,0.0004203345,0.0002081962,0.0001033779,0.000003732305],"category_scores_gemma":[0.0001241462,0.0001434519,0.00003806166,0.00011874,0.00002343835,0.001840392,0.00003388985,0.0003518452,0.000002715242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000149301,"about_ca_system_score_gemma":0.00008119264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003000324,"about_ca_topic_score_gemma":0.000015568,"domain_scores_codex":[0.998705,0.00002438614,0.0005025296,0.0001184486,0.0004707307,0.0001788692],"domain_scores_gemma":[0.998639,0.00003241807,0.0003569314,0.00004960638,0.0007759177,0.0001460659],"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":[0.0008838261,0.0001878008,0.06925593,0.0002559374,0.000343454,0.0001727837,0.008179884,0.5806046,0.2495569,0.00006301633,0.002766646,0.08772921],"study_design_scores_gemma":[0.002897758,0.0001102036,0.04624049,0.000894634,0.00007893853,0.001049454,0.001819439,0.9402776,0.005384235,0.0001132875,0.0007064498,0.0004275587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8537643,0.001190752,0.1442442,0.0001815429,0.0003064242,0.00004836378,0.00001065968,0.00005862055,0.0001951589],"genre_scores_gemma":[0.971007,0.00001805436,0.02825681,0.00008244043,0.0005603944,0.000001573403,0.00002184319,0.00001910605,0.00003281059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3596729,"threshold_uncertainty_score":0.5849798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035021811393668,"score_gpt":0.2737933567860782,"score_spread":0.2387715453924102,"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."}}