{"id":"W2152842511","doi":"10.1109/crv.2012.14","title":"Information Fusion in Visual-Task Inference","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Security token; Task (project management); Inference; Eye movement; Artificial intelligence; Process (computing); Probabilistic logic; Bayesian inference; Visual search; Modalities; Human–computer interaction; Machine learning; Natural language processing; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006284185,0.001272672,0.002587283,0.002979565,0.001085501,0.002386411,0.002873363,0.002316433,0.002161752],"category_scores_gemma":[0.02729909,0.001540057,0.002462021,0.002680462,0.00216911,0.006937682,0.003128283,0.002795812,0.0004732176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002585767,"about_ca_system_score_gemma":0.002163013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01477374,"about_ca_topic_score_gemma":0.01163222,"domain_scores_codex":[0.9962943,0.00132031,0.0002607023,0.0009068178,0.0008300563,0.0003877484],"domain_scores_gemma":[0.9849999,0.01240017,0.000740367,0.0009934221,0.0006171085,0.000249033],"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":[0.0007284539,0.0002911145,0.003373281,0.0003075475,0.000300517,0.0002505259,0.0007185465,0.6248062,0.004893169,0.03733423,0.001355127,0.3256412],"study_design_scores_gemma":[0.00001564115,0.00003777465,0.0005557764,0.00001627898,0.0000315557,0.00003476152,0.00003375915,0.9448299,0.002000135,0.05205883,0.0003583606,0.0000271553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01751182,0.0002768197,0.9804649,0.0002781675,0.00001140474,0.00005988952,0.0001109805,0.0006804999,0.0006054384],"genre_scores_gemma":[0.6851581,0.0003520524,0.3123074,0.0002368865,0.00006144129,0.000227988,0.0005166256,0.0001636867,0.000975852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01477374,"threshold_uncertainty_score":0.03323436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304796934738521,"score_gpt":0.2757817254463903,"score_spread":0.2627337560990051,"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."}}