{"id":"W2920647791","doi":"10.17577/ijertv7is060085","title":"Object Recognition Using Deep Learning","year":2018,"lang":"en","type":"article","venue":"International Journal of Engineering Research and","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Cognitive neuroscience of visual object recognition; Object (grammar); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008043585,0.00006129482,0.00009252039,0.0005126695,0.00005577775,0.0001049184,0.00009659192,0.00006048633,0.00004515871],"category_scores_gemma":[0.0003250354,0.00005785358,0.00003193832,0.0001404164,0.00003085103,0.0002031451,0.00002384059,0.0004081187,0.00001609525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096302,"about_ca_system_score_gemma":0.00001533144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001297001,"about_ca_topic_score_gemma":0.000001153512,"domain_scores_codex":[0.9991157,0.0000266723,0.0002259668,0.00005853979,0.0004232833,0.0001498045],"domain_scores_gemma":[0.9989501,0.0001114908,0.00003715201,0.00003540788,0.0007891712,0.0000766599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005004837,0.0000683723,0.002170081,0.0001162921,0.0008544872,0.0003333617,0.001510621,0.1681657,0.4506201,0.0001509826,0.001046905,0.3744626],"study_design_scores_gemma":[0.002295502,0.001802617,0.002733902,0.001009843,0.00002074028,0.002411917,0.0005320304,0.8600039,0.08990093,0.0007889737,0.03804239,0.0004572242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663612,0.0003201085,0.03130322,0.00001104397,0.001433532,0.00004170952,0.000001201899,0.00003462583,0.000493321],"genre_scores_gemma":[0.996713,0.0001068559,0.001260238,0.000001495998,0.001885671,8.266123e-7,7.066901e-7,0.00001582551,0.00001532031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6918382,"threshold_uncertainty_score":0.23592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07558359506352623,"score_gpt":0.3443859601039807,"score_spread":0.2688023650404545,"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."}}