{"id":"W1832501911","doi":"10.1007/978-3-540-76858-6_6","title":"Nonuniform Segment-Based Compression of Motion Capture Data","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Human Motion and Animation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classification of discontinuities; Lossy compression; Data compression; Wavelet; Algorithm; Compression (physics); Smoothing; Computer science; Data compression ratio; Mathematics; Artificial intelligence; Computer vision; Mathematical analysis; Image compression; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003699498,0.0002203629,0.0002264621,0.0004422694,0.00005717665,0.00005022356,0.0007573335,0.0002107266,0.00009000897],"category_scores_gemma":[0.00001441337,0.0002040641,0.00003687248,0.0001545238,0.0001671351,0.0002089271,0.0001611591,0.0003788196,0.0000156256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001241282,"about_ca_system_score_gemma":0.0000494746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006418106,"about_ca_topic_score_gemma":0.00005991569,"domain_scores_codex":[0.9986312,0.000006082395,0.0003118814,0.0003822998,0.0004618768,0.0002066438],"domain_scores_gemma":[0.9990186,0.00006656912,0.00009880323,0.0006760926,0.00007981843,0.00006012349],"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":[0.00000454161,0.00001798795,0.00003329479,0.0002092922,0.000005889605,0.000008482284,0.0002113025,0.5574719,0.001770203,0.0005402979,0.00007328244,0.4396535],"study_design_scores_gemma":[0.0001901052,0.00002044125,0.0001989129,0.0004758949,0.000006186165,0.000003354274,1.770483e-7,0.9936578,0.003100549,0.001218688,0.0009106343,0.0002172915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002161855,0.0001399858,0.9942768,0.00003032993,0.0005833033,0.0001634769,0.00002984691,0.0001053609,0.004454685],"genre_scores_gemma":[0.8711419,0.00002423457,0.1276629,0.0003060051,0.0003459156,0.00000131025,0.0003316259,0.00005210812,0.0001340303],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8709257,"threshold_uncertainty_score":0.8321493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0383949118119124,"score_gpt":0.2610242476709469,"score_spread":0.2226293358590345,"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."}}