{"id":"W4375854256","doi":"10.1109/cbs55922.2023.10115303","title":"Smart paddleboard and other assistive veyances","year":2023,"lang":"en","type":"article","venue":"","topic":"IoT-based Smart Home Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Joint Attosecond Science Laboratory; University of Toronto","funders":"","keywords":"Paddle; Throttle; Automotive engineering; Tension (geology); Controller (irrigation); Simulation; Engineering; FLEX; Computer science; Aeronautics; Mechanical engineering; Telecommunications; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001231235,0.00008777079,0.0001103573,0.0000734616,0.00003154664,0.00002595238,0.0000473843,0.00004283504,0.000100535],"category_scores_gemma":[0.00001073121,0.00007544737,0.00002117088,0.0001906059,0.00002331387,0.00004622261,0.00001484227,0.00005231455,0.0007787425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001763085,"about_ca_system_score_gemma":0.000004737703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005305954,"about_ca_topic_score_gemma":0.00009467894,"domain_scores_codex":[0.9995162,0.00001397952,0.00009850333,0.0001079713,0.00008842129,0.0001749121],"domain_scores_gemma":[0.9997694,0.0000581745,0.000007778904,0.0001100893,0.000009589985,0.00004497327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003173341,0.00003430196,0.3924336,0.0008840013,0.0005580222,0.00009673124,0.002852922,0.007115282,0.01923176,0.007911989,0.5387611,0.03008857],"study_design_scores_gemma":[0.0008378883,0.00007418232,0.1589245,0.0001169758,0.00002311928,0.00001116543,0.001153947,0.05102648,0.01348399,0.0002346989,0.7733523,0.0007607015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7225319,0.000320471,0.002356246,0.0001400111,0.001782628,0.0002545143,0.00003069584,0.002693736,0.2698899],"genre_scores_gemma":[0.9958459,0.000008440869,0.0002093481,0.00005488825,0.0001222138,0.00002374314,0.000002618432,0.00003123194,0.003701613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2733141,"threshold_uncertainty_score":0.9999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503895767247943,"score_gpt":0.215794373308358,"score_spread":0.2007554156358786,"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."}}