{"id":"W4212799235","doi":"10.1109/tcyb.2021.3123842","title":"A Product Fuzzy Convolutional Network for Detecting Driving Fatigue","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Subnetwork; Artificial intelligence; Fuse (electrical); Pattern recognition (psychology); Convolutional neural network; Robustness (evolution); Fuzzy logic; Noise (video); Electroencephalography; Speech recognition; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0005168395,0.000606325,0.0003322848,0.0004907563,0.0002336982,0.0003463295,0.0007077708,0.0006355734,0.001030571],"category_scores_gemma":[0.0009899522,0.0002386525,0.0004291355,0.0003022259,0.0002614519,0.0006367292,0.0004034727,0.0005324901,0.0001923815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007414561,"about_ca_system_score_gemma":0.0006750489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01306626,"about_ca_topic_score_gemma":0.01404538,"domain_scores_codex":[0.9998507,0.00001646623,0.000009247135,0.00005509453,0.00004068701,0.00002773502],"domain_scores_gemma":[0.9997823,0.00006450672,0.00002209503,0.00001963936,0.00009821112,0.00001330057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004617451,0.0001880935,0.0071948,0.0001500442,0.000171053,0.000194795,0.00009731165,0.4400645,0.03306634,0.005120399,0.002595372,0.5106955],"study_design_scores_gemma":[0.000004153177,0.00004641529,0.0009588437,0.000006790005,0.00002326499,0.00004007905,0.000004517084,0.9943341,0.003502949,0.0006768541,0.0003942594,0.000007826361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1342336,0.001400003,0.8585855,0.0002608101,0.0001532746,0.00007218288,0.0002703957,0.000976822,0.004047324],"genre_scores_gemma":[0.8875529,0.000505258,0.1076692,0.0001324775,0.00004065986,0.00005448409,0.0003023871,0.00002308595,0.003719566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01306626,"threshold_uncertainty_score":0.02598041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03648713954793532,"score_gpt":0.2937060215590457,"score_spread":0.2572188820111104,"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."}}