{"id":"W2951778556","doi":"10.48550/arxiv.1112.0059","title":"Local Naive Bayes Nearest Neighbor for Image Classification","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pattern recognition (psychology); Pooling; k-nearest neighbors algorithm; Artificial intelligence; Naive Bayes classifier; Computer science; Merge (version control); Mathematics; Data mining; Support vector machine","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":[0.000195048,0.0003301234,0.0003252508,0.0002174392,0.0001811521,0.000137318,0.001907213,0.000321485,0.00002162786],"category_scores_gemma":[0.00007859272,0.0003685119,0.0002601423,0.0003987628,0.0002328102,0.0009134568,0.001286362,0.0004447941,0.00006532365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002142231,"about_ca_system_score_gemma":0.0001950636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000663576,"about_ca_topic_score_gemma":0.00001006677,"domain_scores_codex":[0.9980346,0.000075237,0.000224162,0.001210135,0.00007973234,0.0003761344],"domain_scores_gemma":[0.9976598,0.0001411633,0.0003149997,0.00131649,0.0004108843,0.000156639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001661872,0.0002326373,0.0003407058,0.0002446144,0.0001107534,0.0002257878,0.0003492863,0.001064133,0.0009474126,0.9563469,0.00305339,0.03691818],"study_design_scores_gemma":[0.000601269,0.0002748737,0.001274435,0.0001546866,0.00009492218,0.000007054161,0.0001171748,0.3946812,0.02362551,0.5718153,0.006389706,0.0009638857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001652231,0.00008908085,0.9934118,0.00008271717,0.0002935488,0.0006550186,0.00003594314,0.0006151187,0.003164491],"genre_scores_gemma":[0.9261633,0.0003605967,0.07233414,0.0001112723,0.0000767047,0.000007906157,0.00003729386,0.0000293982,0.0008793935],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9245111,"threshold_uncertainty_score":0.9998767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128607061019343,"score_gpt":0.2320981491190197,"score_spread":0.1192374430170854,"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."}}