{"id":"W4309522780","doi":"10.18280/rces.090305","title":"An Efficient and Fast Lightweight-Model with ShuffleNetv2 Based on YOLOv5 for Detection of Hardhat-Wearing","year":2022,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Architecture; Object (grammar); Object detection; Recall; Feature extraction; Feature (linguistics); Machine learning; Computer vision; Pattern recognition (psychology); Psychology; Cognitive psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008540462,0.0001066535,0.0003955357,0.0001173732,0.0003047838,0.000001265778,0.0000862077,0.0000199438,0.000003891757],"category_scores_gemma":[0.00005199028,0.00008205119,0.00004275589,0.0001657153,0.00001959436,0.00002182044,0.00007876004,0.0002019252,4.940847e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008811147,"about_ca_system_score_gemma":0.0001024035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004678393,"about_ca_topic_score_gemma":0.000001358735,"domain_scores_codex":[0.9988402,0.0001109674,0.000379069,0.0001743386,0.0002808946,0.0002145732],"domain_scores_gemma":[0.9987842,0.0006237316,0.0001300739,0.0001722584,0.0002320447,0.0000577365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000228955,0.0001273816,0.001206948,0.06334116,0.00007095043,6.357884e-7,0.0005505015,0.9133402,0.0001468895,0.0006503612,0.0001224267,0.02021366],"study_design_scores_gemma":[0.000501422,0.001066428,0.007354806,0.005362985,0.00002371763,5.15665e-7,0.00004264094,0.9840171,0.00006486594,0.000004812831,0.001471707,0.0000890391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1753519,0.06359665,0.7531016,0.001299762,0.000974703,0.005321039,0.0001219172,0.0001316805,0.0001007784],"genre_scores_gemma":[0.9762196,0.003960534,0.01864355,0.0002795751,0.0001057363,0.0007371585,0.00001166512,0.00002372011,0.00001846113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8008677,"threshold_uncertainty_score":0.334595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0549548402739883,"score_gpt":0.4197468796807065,"score_spread":0.3647920394067182,"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."}}