{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008554493,0.0005159226,0.001209974,0.001819091,0.0003663944,0.0008439187,0.0007020081,0.0005660814,0.00114352],"category_scores_gemma":[0.00103227,0.000286745,0.00099337,0.001366385,0.0003888858,0.0007882144,0.0007488028,0.0004028245,0.0007132316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002530862,"about_ca_system_score_gemma":0.0004972822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002315836,"about_ca_topic_score_gemma":0.005728462,"domain_scores_codex":[0.9994338,0.00005554135,0.00004553357,0.0001364962,0.0002064013,0.0001222346],"domain_scores_gemma":[0.9994956,0.00007670226,0.00004804105,0.000103405,0.0002408827,0.00003532551],"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":[0.001010645,0.0003085622,0.01290364,0.0001786017,0.0002310634,0.0001206687,0.0001181401,0.01831326,0.2139139,0.001094653,0.001852511,0.7499543],"study_design_scores_gemma":[0.00005842091,0.0003608909,0.04642988,0.00002812829,0.0003723864,0.000249397,0.000213289,0.871736,0.07647367,0.001629956,0.002399717,0.00004816369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.376714,0.000442992,0.6170884,0.0001473541,0.0001710349,0.0001567375,0.0003010934,0.001200968,0.003777314],"genre_scores_gemma":[0.8737696,0.0001883999,0.122708,0.00005022519,0.00005236049,0.00005087561,0.0005489779,0.00008198524,0.00254951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002315836,"threshold_uncertainty_score":0.004604697,"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."}}