{"id":"W4413206572","doi":"10.3389/fcomp.2025.1664990","title":"Editorial: Deep learning for industrial applications","year":2025,"lang":"en","type":"editorial","venue":"Frontiers in Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Data science","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":[0.001412934,0.0002764288,0.0004772449,0.0008481367,0.0002953981,0.0003337899,0.0007710655,0.001148921,0.000001149764],"category_scores_gemma":[0.0004054092,0.0002997655,0.0001119206,0.001425327,0.000111703,0.0002311142,0.0001218479,0.001345478,0.000004806582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006415754,"about_ca_system_score_gemma":0.000488037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002109524,"about_ca_topic_score_gemma":0.000004535478,"domain_scores_codex":[0.9975888,0.00004520635,0.0004995799,0.0006081664,0.0007833323,0.0004749199],"domain_scores_gemma":[0.9987226,0.0003800115,0.0001162588,0.0003652353,0.0003215982,0.00009426781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001066816,0.00000562293,0.00001550799,0.00004027694,0.00001016989,2.208665e-7,0.00002941438,0.02815511,0.000003657301,0.000004281645,0.8509883,0.1207368],"study_design_scores_gemma":[0.0005919072,0.00005683052,8.89995e-7,0.0001225069,0.000011364,1.470386e-7,0.000008895861,0.1296178,0.00003491813,0.0001058781,0.8692028,0.000246096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.000001416367,0.0001128308,0.4813569,0.000003225198,0.5177543,0.0005125422,0.000009467168,0.000150815,0.00009856208],"genre_scores_gemma":[0.0001529572,0.00004277269,0.01148131,0.000002827142,0.9877055,0.0003974982,0.00004477381,0.00003026213,0.0001421296],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.4699512,"threshold_uncertainty_score":0.9999455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009369413274543392,"score_gpt":0.2419902230106733,"score_spread":0.2326208097361299,"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."}}