{"id":"W3181114435","doi":"10.48550/arxiv.2107.04902","title":"Industry and Academic Research in Computer Vision","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Citation; Field (mathematics); Preference; Computer science; Work (physics); Distribution (mathematics); Data science; Join (topology); Set (abstract data type); Operations research; Library science; Engineering; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01455621,0.0004972955,0.0009692518,0.02409306,0.002461193,0.0130268,0.0008599718,0.00238588,0.01855536],"category_scores_gemma":[0.05336281,0.0003185108,0.0006855096,0.05527312,0.003045777,0.00693854,0.003797539,0.001736093,0.003758318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003619042,"about_ca_system_score_gemma":0.005092524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003214779,"about_ca_topic_score_gemma":0.004498188,"domain_scores_codex":[0.9821734,0.007907772,0.001215352,0.0021833,0.005213726,0.001306395],"domain_scores_gemma":[0.8637347,0.09318744,0.01652166,0.005239469,0.01544156,0.005875056],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005116973,0.0004836572,0.322051,0.003039517,0.0005215128,0.0007041054,0.008907356,0.001818236,0.001974479,0.2027228,0.03199569,0.4252699],"study_design_scores_gemma":[0.0001086078,0.0004433314,0.4431685,0.002012329,0.0004289745,0.00216213,0.02368229,0.00480894,0.002217573,0.1810803,0.3397409,0.0001460807],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4208828,0.1908067,0.01744918,0.06256295,0.0022818,0.000148887,0.003040656,0.0003587628,0.3024682],"genre_scores_gemma":[0.9396324,0.03477883,0.005415986,0.002154798,0.003112445,0.0001151499,0.001502305,0.0001242385,0.01316384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9854438,"threshold_uncertainty_score":0.07698154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1933225886815539,"score_gpt":0.2626880729983533,"score_spread":0.06936548431679943,"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."}}