{"id":"W2112662632","doi":"10.1002/wics.198","title":"STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling","year":2012,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Principal component analysis; Multidimensional scaling; Linear discriminant analysis; Metric (unit); Similarity (geometry); Computer science; Mathematics; Set (abstract data type); Contingency table; Algorithm; Data mining; Statistics; Artificial intelligence; Image (mathematics)","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":[0.01213102,0.001842584,0.001825079,0.004519597,0.001172803,0.004710406,0.001226992,0.001374957,0.003578],"category_scores_gemma":[0.04008457,0.0009721112,0.001845506,0.007469094,0.002802868,0.00481514,0.004885547,0.0029643,0.001291482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001663425,"about_ca_system_score_gemma":0.002580692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002300787,"about_ca_topic_score_gemma":0.002972407,"domain_scores_codex":[0.9867754,0.007870252,0.0007194264,0.001405897,0.002939226,0.0002898333],"domain_scores_gemma":[0.9852729,0.008821024,0.001251526,0.001623722,0.002719947,0.0003107926],"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":[0.0003618697,0.0001305191,0.00620999,0.0005387828,0.000377619,0.0001937018,0.0006831005,0.1613215,0.003805664,0.3820707,0.008283403,0.4360233],"study_design_scores_gemma":[0.00004191123,0.0002230369,0.003370654,0.000131215,0.00005157388,0.0001367538,0.0003253104,0.6859165,0.002806149,0.2966639,0.01022821,0.0001047337],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01026325,0.0006685124,0.9851039,0.000489877,0.00007814334,0.0001118565,0.0002739363,0.0003324021,0.002678109],"genre_scores_gemma":[0.1552145,0.0007812105,0.8399142,0.0001816564,0.0001564608,0.0004535845,0.0008404749,0.0004844542,0.001973455],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01213102,"threshold_uncertainty_score":0.06415576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05564053156836534,"score_gpt":0.3654869761204985,"score_spread":0.3098464445521332,"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."}}