{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0630943,0.001730121,0.0009988506,0.008256337,0.002540637,0.004173977,0.003265599,0.001728328,0.003482243],"category_scores_gemma":[0.1322291,0.0008447796,0.001499318,0.005130673,0.002804319,0.005032206,0.003413079,0.001342564,0.001151587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003133568,"about_ca_system_score_gemma":0.005736118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004176056,"about_ca_topic_score_gemma":0.005250539,"domain_scores_codex":[0.9200439,0.0418437,0.007683361,0.003777822,0.02534143,0.001309757],"domain_scores_gemma":[0.8849252,0.06176406,0.01041404,0.006257629,0.03577017,0.0008688662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00123374,0.0008998201,0.1877923,0.007455867,0.0004378676,0.0008571177,0.01464988,0.01083399,0.02187733,0.06335842,0.02158603,0.6690176],"study_design_scores_gemma":[0.0002694894,0.005869774,0.3712904,0.008001286,0.001689963,0.00494872,0.05586534,0.2153544,0.09199008,0.1325275,0.1108077,0.001385385],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08353534,0.006687304,0.8868961,0.002445234,0.0006994152,0.002331769,0.0007972174,0.00107232,0.01553539],"genre_scores_gemma":[0.423842,0.002135428,0.5677426,0.0003755476,0.0001386833,0.002039288,0.001040254,0.000192293,0.002493878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0630943,"threshold_uncertainty_score":0.3336785,"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."}}