{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005087779,0.00009740559,0.0001081185,0.0001136674,0.00009529209,0.0004199671,0.0004918717,0.00005679028,0.000008081492],"category_scores_gemma":[0.0001766967,0.00007670143,0.00003782161,0.0003310251,0.00009023935,0.002543756,0.000214174,0.0001421126,0.0001149156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000250048,"about_ca_system_score_gemma":0.00004473688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001378982,"about_ca_topic_score_gemma":0.000003514456,"domain_scores_codex":[0.998861,0.0002437609,0.0003637829,0.0001919348,0.0001826015,0.0001568999],"domain_scores_gemma":[0.9990466,0.0003699161,0.0001566868,0.0002910432,0.00006002731,0.00007567854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007756564,0.0007894234,0.1927052,0.00007037531,0.00007371098,0.00001982927,0.001116627,0.00008901954,0.0002069321,0.1224563,0.001985049,0.6797118],"study_design_scores_gemma":[0.001816875,0.0004274673,0.1472373,0.0005764455,0.000009786761,0.00005194576,0.0002347419,0.02105306,0.006395353,0.818263,0.003368986,0.000565086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5627656,0.00113476,0.4130042,0.000873965,0.0003521577,0.001293014,0.0002524809,0.0003233707,0.0200005],"genre_scores_gemma":[0.9712957,0.00003594661,0.028426,0.00006651656,0.000007603051,0.00002251024,0.0001149586,0.000009011685,0.00002172925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6958067,"threshold_uncertainty_score":0.4049753,"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."}}