{"id":"W2122640592","doi":"10.1002/cem.1245","title":"An efficient algorithm for Parafac with uncorrelated mode‐A components applied to large <i>I</i> × <i>J</i> × <i>K</i> data sets with <i>I</i> &gt;&gt; <i>JK</i>","year":2009,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Algorithm; Uncorrelated; Mode (computer interface); Product (mathematics); Constraint (computer-aided design); Computer science; Mathematics; Statistics","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"],"consensus_categories":[],"category_scores_codex":[0.001068142,0.0004859963,0.0009172675,0.0007578166,0.0002853342,0.0001884811,0.001405522,0.0001856619,0.00001092411],"category_scores_gemma":[0.00009683051,0.0003649247,0.0001348516,0.003078339,0.00005576314,0.0003270404,0.00009680806,0.0005029443,0.00001687878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001450614,"about_ca_system_score_gemma":0.0001543445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001085731,"about_ca_topic_score_gemma":0.000002604253,"domain_scores_codex":[0.9962347,0.0000520459,0.001109548,0.0006119443,0.001300707,0.0006910444],"domain_scores_gemma":[0.9954804,0.0004440577,0.001059017,0.001383071,0.0009303988,0.000703058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.008978172,0.05299754,0.0007874716,0.0006397588,0.002427706,0.0005072586,0.003399687,0.05009611,0.1492981,0.03721752,0.4978116,0.1958391],"study_design_scores_gemma":[0.07004091,0.01608068,0.002209358,0.001387516,0.004487094,0.003876014,0.00151441,0.3674451,0.08071313,0.02199475,0.4231941,0.007056964],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1602864,0.000117229,0.8344994,0.001279965,0.0001487752,0.001411059,0.0009231975,0.0001582842,0.001175721],"genre_scores_gemma":[0.5670236,0.00002712085,0.4285018,0.003711043,0.0002130959,0.00003056046,0.000310168,0.00009965659,0.00008291328],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4067373,"threshold_uncertainty_score":0.9998803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04417632818794799,"score_gpt":0.3319105384430591,"score_spread":0.2877342102551111,"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."}}