{"id":"W4399986240","doi":"10.36548/jismac.2024.2.009","title":"Smart IoT based Accident Monitoring and Rescue System","year":2024,"lang":"en","type":"article","venue":"Journal of ISMAC","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Internet of Things; Computer science; Accident (philosophy); Computer security; Aeronautics; Embedded system; 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":[0.0001386713,0.0003613276,0.0004608273,0.0006742923,0.0003785603,0.000472448,0.0008343252,0.000500767,0.005699986],"category_scores_gemma":[0.0002443728,0.0001565875,0.0002383012,0.0003426387,0.0001226677,0.0005285907,0.0005968548,0.000202439,0.002593705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002482869,"about_ca_system_score_gemma":0.0004410754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001134022,"about_ca_topic_score_gemma":0.0009673149,"domain_scores_codex":[0.9998034,0.00001680922,0.00002452732,0.00005134177,0.00007172087,0.0000321623],"domain_scores_gemma":[0.9998367,0.00001458017,0.00002573794,0.00002635112,0.0000752952,0.00002130066],"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":[0.00188694,0.0009869098,0.02945074,0.001135759,0.0001818331,0.002858105,0.0006024557,0.0521394,0.1790449,0.007910024,0.09534068,0.6284622],"study_design_scores_gemma":[0.0003672485,0.001677382,0.0380257,0.0001995038,0.0003704382,0.003928829,0.0005278538,0.6988151,0.1016608,0.005454832,0.1487708,0.0002015087],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3748024,0.002703957,0.471058,0.001540661,0.001185012,0.001593843,0.004172701,0.04193983,0.1010036],"genre_scores_gemma":[0.9544543,0.0005419284,0.0226892,0.0004675087,0.0001173933,0.0004030299,0.001975621,0.00006972028,0.01928124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005699986,"threshold_uncertainty_score":0.01906836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008162575750769884,"score_gpt":0.2224409485668501,"score_spread":0.2142783728160802,"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."}}