{"id":"W26302103","doi":"10.1037/pas0000140","title":"Coordination and adaptation in impromptu teams","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundación Florencio Fiorini","keywords":"Impromptu; Computer science; Teamwork; Joins; Adaptation (eye); Robot; Adversarial system; Key (lock); Multi-agent system; Human–computer interaction; Domain (mathematical analysis); Knowledge management; Artificial intelligence; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007166757,0.0005868157,0.0003376121,0.001619879,0.002475647,0.003906617,0.002420568,0.0008804745,0.02353931],"category_scores_gemma":[0.01869116,0.0004290316,0.0005157092,0.001803105,0.0019864,0.001909512,0.009066525,0.001021808,0.007703629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002219183,"about_ca_system_score_gemma":0.00461636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003645235,"about_ca_topic_score_gemma":0.004692044,"domain_scores_codex":[0.9910938,0.004468445,0.0005207689,0.001623197,0.001143633,0.001150058],"domain_scores_gemma":[0.9898211,0.001589069,0.0009420958,0.003799158,0.001707328,0.002141293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003553683,0.001019629,0.04589787,0.0004671843,0.00008261358,0.001582198,0.05297317,0.006899714,0.003713728,0.08834478,0.03914971,0.759514],"study_design_scores_gemma":[0.0002571506,0.0008726377,0.04541605,0.0006895917,0.00006559958,0.001234534,0.0579192,0.01889887,0.002136325,0.0933091,0.7791044,0.00009659892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3062747,0.0004622015,0.23217,0.002370403,0.001114534,0.002835553,0.0004405548,0.003500932,0.450831],"genre_scores_gemma":[0.8063065,0.0002638316,0.1042489,0.0004664312,0.0001384111,0.002030547,0.000976684,0.0008366874,0.08473211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02353931,"threshold_uncertainty_score":0.0787468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1242522798037863,"score_gpt":0.3337074939629375,"score_spread":0.2094552141591512,"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."}}