{"id":"W2081556332","doi":"10.1016/j.dsp.2011.04.005","title":"Intent inference via syntactic tracking","year":2011,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Inference; BitTorrent tracker; Parsing; Artificial intelligence; Context-free grammar; Context (archaeology); Rule-based machine translation; Bayesian inference; Grammar; Stochastic context-free grammar; Machine learning; Natural language processing; Bayesian probability; Eye tracking; Tree-adjoining grammar","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002750447,0.001502829,0.001184434,0.003019651,0.001439286,0.003248281,0.002228942,0.001877031,0.01329258],"category_scores_gemma":[0.0148972,0.001373158,0.002842664,0.001714634,0.001505765,0.008785435,0.004490518,0.003862182,0.006242451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015743,"about_ca_system_score_gemma":0.001536947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003774686,"about_ca_topic_score_gemma":0.004637823,"domain_scores_codex":[0.9961688,0.001397306,0.0002788577,0.001013842,0.0008613581,0.0002798971],"domain_scores_gemma":[0.992179,0.004490319,0.0002760269,0.001448222,0.001482239,0.0001241458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007510019,0.0003222135,0.004645015,0.0008292532,0.0003822202,0.0008582909,0.001438109,0.01810074,0.02100244,0.2987688,0.04346462,0.6094373],"study_design_scores_gemma":[0.00007023886,0.000066718,0.001139568,0.000164721,0.0002740949,0.0003391892,0.0003102273,0.4042458,0.01431,0.5639209,0.01506592,0.00009266305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01447127,0.0003906813,0.9628899,0.00109541,0.0003555668,0.0001032285,0.001360759,0.006327347,0.01300584],"genre_scores_gemma":[0.5317728,0.0006741345,0.4482651,0.001076919,0.000582397,0.0002183376,0.006873255,0.001687974,0.008849116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01329258,"threshold_uncertainty_score":0.0444681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04746141919118618,"score_gpt":0.2508380469134892,"score_spread":0.203376627722303,"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."}}