{"id":"W2573582133","doi":"","title":"Gaze Following as Goal Inference: A Bayesian Model","year":2011,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Bayesian inference; Artificial intelligence; Inference; Computer science; Probabilistic logic; Bayesian probability; Graphical model; Machine learning; Psychology; Cognitive science","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":[0.00422667,0.0007206831,0.001576397,0.001075277,0.0007153221,0.001929892,0.003102924,0.002702548,0.004836632],"category_scores_gemma":[0.02199015,0.001042814,0.001482002,0.001273563,0.002049595,0.003538295,0.001360458,0.003226731,0.000876768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002240151,"about_ca_system_score_gemma":0.001964361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03116037,"about_ca_topic_score_gemma":0.02419025,"domain_scores_codex":[0.9986926,0.0005886978,0.00004548596,0.0003026037,0.0002240145,0.000146593],"domain_scores_gemma":[0.9912546,0.007106601,0.0005579287,0.0004092365,0.0004622655,0.0002092969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002329162,0.00009892679,0.004009945,0.00009721584,0.0001100166,0.0001952069,0.0004084673,0.7071558,0.001060117,0.2501416,0.002621177,0.03386855],"study_design_scores_gemma":[0.00003021777,0.00001696352,0.0004640508,0.00001228556,0.00001723197,0.00003449612,0.00001693518,0.9209359,0.000117924,0.07795294,0.0003843181,0.00001684458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06077116,0.0004892594,0.9307281,0.001723653,0.00005060598,0.000086468,0.000418678,0.0004231643,0.005308904],"genre_scores_gemma":[0.8017576,0.0009342968,0.1859588,0.0004019157,0.0001191144,0.0003712642,0.0005406114,0.0001376501,0.00977881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03116037,"threshold_uncertainty_score":0.06195796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981265076546814,"score_gpt":0.227448512301929,"score_spread":0.2076358615364608,"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."}}