{"id":"W4240954477","doi":"10.1016/b978-0-08-044894-7.01348-8","title":"Multidimensional Scaling","year":2010,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Multidimensional scaling; Similarity (geometry); Set (abstract data type); Multidimensional analysis; Computer science; Variety (cybernetics); Space (punctuation); Scaling; Object (grammar); Multidimensional data; Scale (ratio); Data set; Data mining; Theoretical computer science; Mathematics; Artificial intelligence; Geography; Machine learning; Cartography; Image (mathematics); Statistics; Geometry","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001399667,0.000307344,0.0003707568,0.00009645197,0.0001848806,0.00002617172,0.0001849053,0.0004158948,0.001425869],"category_scores_gemma":[0.00002158014,0.0002884468,0.0002508589,0.000006030916,0.00011727,0.00001628443,0.00008961032,0.0007439255,0.001104194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003213004,"about_ca_system_score_gemma":0.00005005443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.771306e-8,"about_ca_topic_score_gemma":0.000005753446,"domain_scores_codex":[0.9987511,0.00001010798,0.0004139752,0.0003498126,0.000289022,0.000185984],"domain_scores_gemma":[0.9987053,0.0001942554,0.0002341957,0.0006028768,0.0001280903,0.000135249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003483846,0.00001700289,2.860705e-7,0.00003657133,0.00004570501,0.000008064468,0.00007012275,6.169106e-8,0.001441492,0.5691955,0.0005137242,0.428668],"study_design_scores_gemma":[0.0001236634,0.00000504895,0.000001958968,0.0001130009,0.00006308947,0.00002087795,0.000001529624,0.000006540904,0.0003643872,0.323371,0.6757028,0.0002260504],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0002103835,0.0000611021,0.00008917123,0.0001666998,0.0002326602,0.0004338927,0.00005417542,0.0001987519,0.9985532],"genre_scores_gemma":[0.0002745861,0.000007056473,0.04818783,0.0003298717,0.0003638503,0.00004121747,0.00003630829,0.0001026305,0.9506567],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6751891,"threshold_uncertainty_score":0.9999568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03594702329380341,"score_gpt":0.3001545632784299,"score_spread":0.2642075399846265,"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."}}