{"id":"W4393284193","doi":"10.2316/j.2024.206-1058","title":"CONSISTENCY ANALYSIS AND SUGGESTIONS OF COLLISION MEASUREMENT IN HUMAN–ROBOT COLLABORATION SAFETY EVALUATION, 1-13.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Commission of Shanghai Municipality","keywords":"Consistency (knowledge bases); Collision; Computer science; Robot; Collision avoidance; Risk analysis (engineering); Human–computer interaction; Computer security; Business; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004511893,0.0000887806,0.0002592752,0.001062996,0.0001934857,0.00004211813,0.00009461067,0.0001014946,0.0001071032],"category_scores_gemma":[0.0009581752,0.00007741348,0.00005851196,0.0007460033,0.00005621561,0.000320393,0.00003941755,0.0002753315,0.000002391286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004738403,"about_ca_system_score_gemma":0.001015183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001496019,"about_ca_topic_score_gemma":0.0005950003,"domain_scores_codex":[0.9967635,0.0004568739,0.001202877,0.0001415366,0.00130349,0.0001317673],"domain_scores_gemma":[0.9947765,0.0006066643,0.0004372183,0.00007646567,0.004012929,0.00009022551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008258619,0.0005577785,0.5268568,0.001285926,0.001946293,0.00004949976,0.007830472,0.2505323,0.006816324,0.1143016,0.00110724,0.0878899],"study_design_scores_gemma":[0.001006203,0.0001136942,0.7843969,0.0007976054,0.0002153345,0.000008648761,0.0005155597,0.2091408,0.00005383463,0.003448192,0.0002293675,0.00007380056],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.896881,0.005113149,0.07678522,0.01627469,0.002047753,0.001318956,0.0001300376,0.00003096931,0.001418184],"genre_scores_gemma":[0.9969358,0.0006316899,0.002130886,0.00004972291,0.0001355906,0.00001541611,0.00006319701,0.000006976324,0.00003070971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2575401,"threshold_uncertainty_score":0.315683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07686404113111628,"score_gpt":0.4754715479960636,"score_spread":0.3986075068649473,"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."}}