{"id":"W2095994713","doi":"","title":"The computational costs of recipient design and intention recognition in communication","year":2011,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Task (project management); Inference; Computer science; Cognitive psychology; Psychology; Work (physics); Human–computer interaction; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.006525238,0.0007203686,0.0008316815,0.001037156,0.001656557,0.006858489,0.001808297,0.002980901,0.01752309],"category_scores_gemma":[0.05354429,0.001252874,0.001350024,0.00121321,0.004769189,0.01411836,0.005021543,0.002849945,0.002514633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001691459,"about_ca_system_score_gemma":0.001447319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002180775,"about_ca_topic_score_gemma":0.001458867,"domain_scores_codex":[0.9927276,0.004232065,0.0003947617,0.0009422262,0.001194466,0.0005088858],"domain_scores_gemma":[0.9494761,0.04052648,0.00200872,0.006242873,0.001214344,0.0005314182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001140842,0.000307115,0.007747953,0.000539025,0.0001220234,0.0006621549,0.003372978,0.04936388,0.01096407,0.6788471,0.003337944,0.2435949],"study_design_scores_gemma":[0.0001367235,0.000233708,0.008742443,0.00009773034,0.0001873057,0.001068162,0.001515426,0.281083,0.009360204,0.6896062,0.007822563,0.0001466547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3500507,0.001141817,0.5508625,0.009136987,0.0002490525,0.000177829,0.0002353355,0.00122192,0.08692387],"genre_scores_gemma":[0.8976911,0.0005000366,0.09243703,0.0002600506,0.00009118396,0.000169933,0.0002361994,0.0002644302,0.008350022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01752309,"threshold_uncertainty_score":0.05862057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03999759207546438,"score_gpt":0.2171733869506212,"score_spread":0.1771757948751568,"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."}}