{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001729881,0.0004850244,0.0003827597,0.0002515413,0.0002562104,0.002952723,0.002602172,0.0002318191,0.0003575849],"category_scores_gemma":[0.0002241008,0.0004350577,0.0003159733,0.0009323379,0.00008891639,0.0130938,0.0009561419,0.0005848805,0.00331698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003093518,"about_ca_system_score_gemma":0.0005051986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005374424,"about_ca_topic_score_gemma":8.090415e-7,"domain_scores_codex":[0.9969776,0.00005302333,0.0005837083,0.0009172549,0.0005956191,0.0008728133],"domain_scores_gemma":[0.9980655,0.00008462355,0.0001799328,0.0009855968,0.00005119644,0.0006330927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001263243,0.0009280623,0.1347457,0.0001856797,0.0001863924,0.0008313491,0.001220317,0.00005582989,0.0002567109,0.7719198,0.002418988,0.08712488],"study_design_scores_gemma":[0.000888684,0.0003059964,0.003213562,0.0002787247,0.00002810419,0.0000690899,0.00004819188,0.04359521,0.004451094,0.9355749,0.01002019,0.001526259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04448578,0.0001716907,0.7194773,0.0005933956,0.0002650906,0.0003142971,0.0002177907,0.00138195,0.2330927],"genre_scores_gemma":[0.9443532,0.00001132416,0.05299364,0.001152626,0.0000608689,0.00003428199,0.00004870303,0.00006080236,0.001284536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8998674,"threshold_uncertainty_score":0.9998101,"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."}}