{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000210616,0.0007867248,0.0003058941,0.0004771858,0.0002285457,0.0008365911,0.0009326604,0.0007746595,0.01067379],"category_scores_gemma":[0.0007036967,0.0001558866,0.0003365738,0.000353804,0.0003844044,0.001231661,0.0009896476,0.0004130347,0.002112905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001402697,"about_ca_system_score_gemma":0.0001375911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005538376,"about_ca_topic_score_gemma":0.0006105993,"domain_scores_codex":[0.9997484,0.00003773743,0.00001533672,0.00005723222,0.0001035321,0.00003771573],"domain_scores_gemma":[0.9998034,0.00005711873,0.00001558617,0.00003198615,0.00006191967,0.00002990558],"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":[0.0006868602,0.0002553097,0.002316853,0.001346995,0.00008022023,0.001386344,0.0005080975,0.005116332,0.1245932,0.03603589,0.02213816,0.8055357],"study_design_scores_gemma":[0.0001629435,0.002936088,0.008761746,0.0006809629,0.0001837403,0.005082451,0.0006277274,0.06899183,0.1272338,0.02425199,0.7608462,0.0002405851],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1206025,0.02349754,0.6873075,0.0025794,0.003143886,0.0005196548,0.0009040454,0.006558897,0.1548865],"genre_scores_gemma":[0.7459168,0.008520489,0.1443151,0.002178263,0.0005770411,0.0003539911,0.0008275785,0.0002259543,0.09708467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01067379,"threshold_uncertainty_score":0.03570741,"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."}}