{"id":"W2781405137","doi":"10.3390/s17122955","title":"Anti-Runaway Prevention System with Wireless Sensors for Intelligent Track Skates at Railway Stations","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Track (disk drive); Real-time computing; Wireless; Engineering; Global Positioning System; Simulation; Management system; Computer security; Computer science; Embedded system; Automotive engineering; Transport engineering; Telecommunications; Operations management","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":[0.0003045125,0.0006077996,0.0005606784,0.0008213221,0.0003403359,0.0004393882,0.0008586249,0.0004504841,0.001418536],"category_scores_gemma":[0.0004652297,0.0003514313,0.0002887944,0.0005388121,0.0001527219,0.000829745,0.0007132419,0.0002676685,0.0007200656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002490653,"about_ca_system_score_gemma":0.0004142895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002065945,"about_ca_topic_score_gemma":0.003125379,"domain_scores_codex":[0.9994696,0.00006669614,0.00004500547,0.0001453942,0.0002205798,0.00005271487],"domain_scores_gemma":[0.9996454,0.00003832241,0.0000577581,0.00004945007,0.0001822489,0.00002685773],"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":[0.001385845,0.0006356977,0.08850751,0.000984495,0.0002946491,0.001112225,0.001362264,0.01590747,0.3934141,0.001316793,0.01148323,0.4835957],"study_design_scores_gemma":[0.0002359923,0.003062766,0.1574183,0.0002092171,0.001067287,0.002106957,0.001587248,0.3881858,0.3839331,0.001190587,0.06069827,0.0003044739],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6176157,0.001247553,0.3561747,0.0003029928,0.0003115066,0.0003871665,0.000763916,0.01251519,0.01068121],"genre_scores_gemma":[0.9592288,0.000317293,0.03459581,0.0001071864,0.00003655148,0.0001389905,0.0003899659,0.00005241557,0.005132988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002065945,"threshold_uncertainty_score":0.004745483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01259268986350682,"score_gpt":0.2324921179770997,"score_spread":0.2198994281135929,"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."}}