{"id":"W4289518813","doi":"10.1007/s11276-022-03076-9","title":"Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things","year":2022,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Høgskulen på Vestlandet","keywords":"Computer science; Frame (networking); Dependability; Real-time computing; Variety (cybernetics); Frame rate; Object (grammar); The Internet; Artificial intelligence; Simulation; Telecommunications; World Wide Web","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":[0.0005370596,0.0005055746,0.0005760122,0.001583225,0.0003242315,0.0004423064,0.0006828611,0.0006137866,0.0007392295],"category_scores_gemma":[0.0006764695,0.0002452549,0.0005966173,0.0008925424,0.0001980629,0.0005750766,0.0004915079,0.0004456341,0.0003506396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003855805,"about_ca_system_score_gemma":0.0003825256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006563133,"about_ca_topic_score_gemma":0.008327344,"domain_scores_codex":[0.9997448,0.0000312481,0.00001299822,0.000068348,0.00007828996,0.00006432371],"domain_scores_gemma":[0.9997647,0.00005626277,0.00002499638,0.00002955704,0.0001057303,0.00001869726],"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":[0.0004644115,0.0003212905,0.01046578,0.00009543707,0.0001574423,0.0003659943,0.0001198699,0.1074739,0.05291056,0.002301967,0.003590613,0.8217327],"study_design_scores_gemma":[0.000003803031,0.00005322522,0.002690822,0.000005584764,0.00001583379,0.00006922989,0.00002586483,0.9876386,0.008082438,0.0006317315,0.000773549,0.000009405412],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2914523,0.0005608858,0.7028248,0.0002231477,0.0001858123,0.00009038452,0.0002338969,0.001747565,0.002681185],"genre_scores_gemma":[0.8656581,0.0001830935,0.1309594,0.00009032893,0.00004649526,0.00004812767,0.0004260091,0.00005423166,0.00253418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006563133,"threshold_uncertainty_score":0.01304984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01491400553816413,"score_gpt":0.2447276407495811,"score_spread":0.229813635211417,"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."}}