{"id":"W6981395268","doi":"","title":"Efficient and Scalable Techniques for Multivariate Time Series&#13;\\nAnalysis and Search","year":2017,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Univariate; Dimensionality reduction; Curse of dimensionality; Feature selection; Principal component analysis; Preprocessor; Pattern recognition (psychology); Ranking (information retrieval); Scalability","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","sts"],"consensus_categories":[],"category_scores_codex":[0.002431107,0.000246234,0.0005257196,0.001212437,0.005394158,0.0008630469,0.0008056613,0.0002919987,0.00005992197],"category_scores_gemma":[0.0003884882,0.000265666,0.0001948995,0.0005881449,0.0009057728,0.0003484769,0.0002551452,0.0005939641,0.000008816035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003101273,"about_ca_system_score_gemma":0.0008721425,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09455405,"about_ca_topic_score_gemma":0.07920783,"domain_scores_codex":[0.9964342,0.0008251431,0.0002267697,0.0008747588,0.0008159055,0.0008232158],"domain_scores_gemma":[0.9979177,0.0004794848,0.0002064692,0.0005707777,0.0004123426,0.0004132918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01028395,0.00214688,0.1868481,0.005409834,0.01059839,0.003737734,0.09588204,0.0001734535,0.2092779,0.150902,0.01747832,0.3072613],"study_design_scores_gemma":[0.002785387,0.001517263,0.1805457,0.002079551,0.002375396,0.0000224246,0.07142642,0.01140721,0.05262693,0.004118588,0.6672508,0.00384427],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6983534,0.0002088526,0.00009779254,0.0006781409,0.0001574757,0.001023313,0.0000866521,0.0001107542,0.2992837],"genre_scores_gemma":[0.6605042,0.0006395495,0.0002032414,0.000002334725,0.0003189215,0.000009747547,0.0001441599,0.00003450323,0.3381433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6497725,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322202498340901,"score_gpt":0.3213570178707711,"score_spread":0.2981349928873621,"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."}}