{"id":"W3165449177","doi":"10.18757/ejtir.2021.21.2.5354","title":"How to ensure control of cooperative vehicle and truck platoons using Meaningful Human Control","year":2021,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Traffic control and management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Platoon; Control (management); Truck; Automation; Perspective (graphical); Set (abstract data type); Risk analysis (engineering); Liability; Automotive industry; Transport engineering; Engineering; Computer science; Computer security; Operations research; Business; Automotive 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008787255,0.0001528441,0.0003944085,0.0002002441,0.000122893,0.00007748401,0.0001426209,0.00003340705,0.00003344716],"category_scores_gemma":[0.0000404393,0.0001269344,0.0000710665,0.0002089429,0.0001065352,0.0001552007,0.00001896859,0.0005002685,3.328339e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002390957,"about_ca_system_score_gemma":0.00004270132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002044233,"about_ca_topic_score_gemma":0.00001315772,"domain_scores_codex":[0.9985868,0.0002198727,0.0003828972,0.0001516349,0.0003760774,0.0002827249],"domain_scores_gemma":[0.9990914,0.00006652821,0.00006141433,0.0001271762,0.0004244658,0.0002289678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006371327,0.00007135121,0.02008563,0.0005242331,0.00148921,0.003068939,0.005936758,0.1452661,0.7718794,0.001872467,0.00141448,0.04775432],"study_design_scores_gemma":[0.01644419,0.001602235,0.9156634,0.0006139842,0.0004092196,0.0005902796,0.003289794,0.00880579,0.006144253,0.0001879041,0.04559653,0.0006523928],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782364,0.001824579,0.01821054,0.000426397,0.0001034861,0.0001775696,0.00003621278,0.00001612887,0.0009687045],"genre_scores_gemma":[0.9989647,0.0001049557,0.0006431341,0.00004428199,0.0001606642,5.895942e-7,0.000001789677,0.00003085592,0.00004901392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8955778,"threshold_uncertainty_score":0.5176235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02013146584438362,"score_gpt":0.2523980799943086,"score_spread":0.232266614149925,"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."}}