{"id":"W7058346563","doi":"","title":"Multi-Valued Model Checking IoT and Intelligent&#13;\\nSystems with Trust and Commitment Protocols","year":2024,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Magnetic Field Sensors Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Concordia University","keywords":"Model checking; Context (archaeology); Reliability (semiconductor); Formal verification; Temporal logic; Internet of Things; Linear temporal logic; Description logic; Trust management (information system); Consistency (knowledge bases)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003308032,0.0004022617,0.0004602831,0.0009514862,0.0002810635,0.0002904133,0.000355184,0.0004210316,0.00001251399],"category_scores_gemma":[0.00001759388,0.000410326,0.00007249475,0.0004615081,0.0001860998,0.0001072484,0.0001287264,0.001365058,0.00000520882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004438267,"about_ca_system_score_gemma":0.0002043383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003934822,"about_ca_topic_score_gemma":0.01107262,"domain_scores_codex":[0.9978427,0.000159142,0.0002787872,0.0006473562,0.0005231959,0.0005488617],"domain_scores_gemma":[0.9989895,0.00006933587,0.00006505607,0.0004927489,0.0001303438,0.0002530362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01154822,0.001990044,0.09399358,0.144628,0.01066417,0.03092769,0.1120066,0.01857623,0.4567436,0.05709807,0.02956061,0.03226313],"study_design_scores_gemma":[0.002161974,0.002072549,0.01121065,0.006008646,0.0004033866,0.0001970273,0.01234285,0.739215,0.2038703,0.000606125,0.01945345,0.002458038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9150848,0.0006548307,0.0007419608,0.00003956824,0.0002321338,0.01092977,0.00001365933,0.000744702,0.0715586],"genre_scores_gemma":[0.9518949,0.0003395514,0.001048184,0.000002437132,0.0001322458,0.0004439108,0.00002985293,0.0001439032,0.04596505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7206388,"threshold_uncertainty_score":0.9998348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03687699727560405,"score_gpt":0.2959814917344075,"score_spread":0.2591044944588035,"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."}}