{"id":"W1594577898","doi":"10.48550/arxiv.0908.2661","title":"Human-Robot Teams in Entertainment and Other Everyday Scenarios","year":2009,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Teamwork; Robot; Entertainment; Human–computer interaction; Robotics; Artificial intelligence; Computer science; Everyday life; Set (abstract data type); Architecture; Focus (optics); Human–robot interaction; Knowledge management; Management; Political science","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"],"consensus_categories":[],"category_scores_codex":[0.000462977,0.0002853482,0.000338783,0.0001752557,0.00008825427,0.0001425954,0.0006588738,0.0002217978,0.00002616204],"category_scores_gemma":[0.00001436338,0.000263997,0.00008307777,0.0001037888,0.00002462363,0.0001671307,0.0006316912,0.000367487,0.00007509919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001733821,"about_ca_system_score_gemma":0.00005432549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009417115,"about_ca_topic_score_gemma":0.0002573122,"domain_scores_codex":[0.9980295,0.0001498526,0.0004615941,0.000767819,0.0002812124,0.0003099848],"domain_scores_gemma":[0.9988063,0.00002407924,0.0002514798,0.0007974594,0.00002963922,0.00009106842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004189395,0.0002447742,0.9861794,0.0001353246,0.00003930261,0.0000320767,0.00310939,0.001246628,0.00306492,0.0009827531,0.0006309934,0.004330324],"study_design_scores_gemma":[0.0006480355,0.00006170454,0.9861975,0.0005002787,0.000009321477,0.000006260224,0.00003576563,0.008467503,0.0006393727,0.0004603412,0.002535924,0.0004380321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779189,0.0003653891,0.01908136,0.0005672596,0.0006287911,0.0006103527,0.000002852863,0.0001044422,0.0007206021],"genre_scores_gemma":[0.9970477,0.0000287963,0.001265381,0.0007096944,0.0001530276,0.00004376488,0.000005536457,0.00001661292,0.000729445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01912879,"threshold_uncertainty_score":0.9999812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04759165334662942,"score_gpt":0.2925875564465362,"score_spread":0.2449959030999068,"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."}}