{"id":"W1817149645","doi":"10.1007/978-3-642-21593-3_34","title":"Dictionary Learning in Texture Classification","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Texture (cosmology); Dictionary learning; Pattern recognition (psychology); Natural language processing; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008552097,0.0003884795,0.0003566984,0.001088698,0.0002008136,0.000266,0.002339585,0.0004212129,0.00003938181],"category_scores_gemma":[0.00009398055,0.0003616778,0.00009775227,0.0008900709,0.0004760435,0.0007556218,0.0006306653,0.001306506,0.00007709795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000379678,"about_ca_system_score_gemma":0.0004488709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001958033,"about_ca_topic_score_gemma":0.00001927633,"domain_scores_codex":[0.9969422,0.00005369663,0.0005295696,0.001310992,0.0007068075,0.0004567497],"domain_scores_gemma":[0.9981754,0.0001988039,0.0003203325,0.0009720027,0.0002264835,0.000106967],"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":[0.000004611633,0.00003323312,0.0002737989,0.00001905691,0.000003087793,0.00003025274,0.0004852524,0.0002238765,0.0005776748,0.05913488,0.00001257856,0.9392017],"study_design_scores_gemma":[0.0003081985,0.0002844768,0.007986836,0.000607721,0.000007604974,0.000102961,6.341966e-7,0.5124007,0.005927233,0.4570613,0.01410654,0.00120581],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000009636416,0.0003942537,0.9845688,0.0008665993,0.0005659822,0.0003028101,0.000001242348,0.0003902679,0.01290035],"genre_scores_gemma":[0.6829408,0.0005991813,0.3094724,0.001520911,0.0005675049,0.00005596065,0.00002437387,0.00007781387,0.00474112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9379959,"threshold_uncertainty_score":0.9998835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02817092203169497,"score_gpt":0.2519435173351485,"score_spread":0.2237725953034536,"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."}}