{"id":"W2619249253","doi":"10.1007/978-3-319-59876-5_37","title":"A Better Trajectory Shape Descriptor for Human Activity Recognition","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Pattern recognition (psychology); Trajectory; Representation (politics); Feature vector; Cluster analysis; Set (abstract data type); Sparse approximation; Feature (linguistics); Margin (machine learning); Computation; Computer vision; Algorithm; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007095985,0.000499098,0.000486141,0.0007500632,0.0009632278,0.001067178,0.002200945,0.0003912842,0.0000788711],"category_scores_gemma":[0.0000740602,0.0004970124,0.000247697,0.0001070561,0.0004852228,0.001429598,0.0004350121,0.0006988889,0.0000921647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002859487,"about_ca_system_score_gemma":0.0002995215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002202086,"about_ca_topic_score_gemma":0.0001340107,"domain_scores_codex":[0.996858,0.0000345043,0.0003796647,0.001507776,0.0006155346,0.0006045446],"domain_scores_gemma":[0.9976064,0.0002546139,0.0004727116,0.001176243,0.000327471,0.0001625483],"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":[0.000007982109,0.00003838557,0.000007980408,0.00004843649,0.00001291193,0.00001811661,0.0002092193,0.00003923549,0.001796499,0.000514172,0.00008742562,0.9972196],"study_design_scores_gemma":[0.001578603,0.0009208248,0.0009955423,0.001372153,0.00006175103,0.0001106817,2.233683e-7,0.2065035,0.03029272,0.7450835,0.01063552,0.002445009],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002527948,0.0000512366,0.990475,0.0004859479,0.002081745,0.0007050576,0.00002772735,0.0002003266,0.003445036],"genre_scores_gemma":[0.6604331,0.00003696554,0.3294324,0.004044841,0.003802156,0.0001735257,0.00008187597,0.0001240311,0.001871062],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9947746,"threshold_uncertainty_score":0.9999698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05910630769663831,"score_gpt":0.2811499826973773,"score_spread":0.222043675000739,"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."}}