{"id":"W4281716694","doi":"10.1088/1742-6596/2278/1/011001","title":"Preface","year":2022,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Library science; Postponement; Political science; Engineering; Computer science; Law","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":[0.0001552714,0.00006108594,0.0001399737,0.00003640971,0.0000895479,0.00003499564,0.00009558241,0.00001813336,0.0001478361],"category_scores_gemma":[0.000007991401,0.00005616242,0.00006294133,0.000125669,0.0000113124,0.0002527737,0.00002357217,0.0002692032,0.000005146746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004693041,"about_ca_system_score_gemma":0.00004576657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002691704,"about_ca_topic_score_gemma":5.966859e-7,"domain_scores_codex":[0.9994306,0.00003351915,0.0002050838,0.00003505439,0.0002152188,0.000080531],"domain_scores_gemma":[0.9996965,0.00001349926,0.0000985362,0.00006845767,0.00009214049,0.00003089723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002947795,0.00010414,0.0003526788,0.000111445,0.0002879603,0.00006817086,0.005354239,0.2032194,0.2219632,0.03259013,0.02065857,0.5149953],"study_design_scores_gemma":[0.001240371,0.001612456,0.0005831638,0.00007853204,0.00005343123,0.0005645904,0.006540211,0.002529821,0.6631399,0.0141424,0.3090163,0.0004988221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194738,0.0003057804,0.06127648,0.0001438648,0.00616208,0.0001687521,0.00002507346,0.0001302787,0.01231385],"genre_scores_gemma":[0.9993081,0.00001301591,0.00007847432,0.000007119136,0.000337146,0.000002883589,4.784896e-7,0.000009152865,0.0002436334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5144964,"threshold_uncertainty_score":0.2290237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02483030253314729,"score_gpt":0.2174981071225577,"score_spread":0.1926678045894105,"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."}}