{"id":"W1759368530","doi":"","title":"Automatic Texture Feature Extraction Inspired by Natural Vision System HMAX Algorithm","year":2012,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Texture (cosmology); Natural (archaeology); Feature extraction; Feature (linguistics); Computer vision; Algorithm; Image (mathematics); Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0005108346,0.0001429633,0.0002933041,0.00008883228,0.0001816276,0.00004413677,0.0002668359,0.0001587431,5.610121e-7],"category_scores_gemma":[0.00002931182,0.00009284697,0.00005398841,0.0002104654,0.00006298599,0.0007181208,0.00009898595,0.0007093906,0.000002692859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007502011,"about_ca_system_score_gemma":0.00001718135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.207953e-7,"about_ca_topic_score_gemma":1.013473e-8,"domain_scores_codex":[0.9989532,0.0000379105,0.0003529896,0.0001231985,0.0003379624,0.0001947936],"domain_scores_gemma":[0.9991288,0.00009198464,0.0004962871,0.00009780248,0.00009740348,0.00008776432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001283167,0.00004101354,0.00008722577,0.0001405044,0.0001121012,0.000002506375,0.001933286,1.759489e-7,0.06914672,0.003849227,0.0296255,0.8950489],"study_design_scores_gemma":[0.005620798,0.001226406,0.03870829,0.00347283,0.0008004488,0.005855539,0.03928717,0.05504194,0.6389509,0.005698673,0.2022586,0.00307832],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05981193,0.1854611,0.7434402,0.007368339,0.002028684,0.000546849,0.000006825416,0.0005035298,0.0008325707],"genre_scores_gemma":[0.9731606,0.002476719,0.02367577,0.0001831505,0.0003354069,0.000005822034,6.381368e-7,0.000006474018,0.0001554074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9133487,"threshold_uncertainty_score":0.3786189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0142350685920855,"score_gpt":0.306218864637941,"score_spread":0.2919837960458554,"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."}}