{"id":"W4309561675","doi":"10.1145/3570732","title":"Integrating Robot Manufacturer Perspectives into Legible Factory Robot Light Communications","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Human-Robot Interaction","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Robot; Robotics; Artificial intelligence; Factory (object-oriented programming); Engineering; Computer science; Human–computer interaction","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004067105,0.0005796881,0.0005257911,0.0009785705,0.004055944,0.0002438798,0.001561512,0.0002525892,0.04029109],"category_scores_gemma":[0.00008189707,0.0006393307,0.000610234,0.0006521408,0.0002216692,0.0008288075,0.0001207111,0.003163699,0.001195205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648683,"about_ca_system_score_gemma":0.00008544803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003041581,"about_ca_topic_score_gemma":0.001950657,"domain_scores_codex":[0.9959332,0.0009691631,0.0008980802,0.0009930173,0.0005911811,0.0006153976],"domain_scores_gemma":[0.9958839,0.0007261907,0.0005112311,0.002417989,0.000254015,0.0002066902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.002965934,0.01883981,0.0008189057,0.0001154919,0.004744729,0.0001107592,0.4475923,0.07368727,0.1127888,0.04989009,0.04790796,0.240538],"study_design_scores_gemma":[0.004387526,0.002979414,0.007924417,0.0002693264,0.0007288745,0.0005185208,0.5823067,0.0009157368,0.01324392,0.00715242,0.3765081,0.003065089],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08229627,0.00233378,0.605458,0.05371043,0.03642385,0.004495427,0.0002452686,0.004125303,0.2109117],"genre_scores_gemma":[0.9754401,0.00006905984,0.00295648,0.001084756,0.0003354369,0.00114393,0.0001542205,0.0001362575,0.01867974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8931438,"threshold_uncertainty_score":0.9996058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1293575145875827,"score_gpt":0.4366394988583532,"score_spread":0.3072819842707705,"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."}}