{"id":"W3122003762","doi":"10.48550/arxiv.1504.01954","title":"Image Subset Selection Using Gabor Filters and Neural Networks","year":2015,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Landmark; Artificial intelligence; Pattern recognition (psychology); Gabor filter; Computer science; Selection (genetic algorithm); Computer vision; Set (abstract data type); Image (mathematics); Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001449761,0.0001039443,0.00009259982,0.0001358815,0.0001298962,0.0001075596,0.0002462494,0.00005853152,0.000004301585],"category_scores_gemma":[0.0000146703,0.0001167022,0.0000355477,0.0006443418,0.00005489455,0.001039649,0.000140047,0.0001262308,0.000004064379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008823396,"about_ca_system_score_gemma":0.00002844073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001493171,"about_ca_topic_score_gemma":0.00002048898,"domain_scores_codex":[0.9992887,0.00007057288,0.00007184387,0.0003271874,0.00003979218,0.0002019253],"domain_scores_gemma":[0.9994875,0.00002041417,0.0000555413,0.0002053375,0.0001101538,0.0001210426],"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":[0.001054349,0.0008131574,0.08193563,0.0001829186,0.0004495195,0.004454771,0.00479971,0.676971,0.03039178,0.0612717,0.03496674,0.1027088],"study_design_scores_gemma":[0.0002433028,0.00008337665,0.000315702,0.000004046937,0.00001032355,0.0000423923,0.00004537816,0.9941899,0.003779731,0.001016218,0.000126786,0.0001428839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2149487,0.00001693997,0.7843267,0.00002679957,0.0001243773,0.00006361639,4.879226e-7,0.0002566953,0.0002357009],"genre_scores_gemma":[0.9925164,0.00001271598,0.00711699,0.0001474308,0.00004628507,1.762319e-7,6.277301e-7,0.000006633697,0.0001527187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7775677,"threshold_uncertainty_score":0.4758978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05757024959037847,"score_gpt":0.1892584499240816,"score_spread":0.1316882003337032,"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."}}