{"id":"W2079201387","doi":"10.1007/s11760-009-0145-0","title":"A criterion for measuring the separability of clusters and its applications to principal component analysis","year":2009,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Principal component analysis; Curse of dimensionality; Dimensionality reduction; Dimension (graph theory); Pattern recognition (psychology); Computer science; Set (abstract data type); Feature (linguistics); Simple (philosophy); Feature vector; Data set; Intrinsic dimension; Space (punctuation); Artificial intelligence; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002397874,0.0001021205,0.0002203077,0.0001002143,0.000218985,0.00008331271,0.0000978813,0.00003526617,0.00002106874],"category_scores_gemma":[0.0000638478,0.00007718411,0.00007035424,0.0004927078,0.00004811359,0.0001225491,0.00003283126,0.0000668399,2.524482e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002070354,"about_ca_system_score_gemma":0.00001951639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009054726,"about_ca_topic_score_gemma":0.000003087469,"domain_scores_codex":[0.9992567,0.00001060239,0.0002194414,0.0002464382,0.000119466,0.0001473996],"domain_scores_gemma":[0.9994829,0.0001126085,0.00009671028,0.0001143184,0.0001251433,0.00006833472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000087427,0.00008336776,0.0009740661,0.0004447383,0.0001310032,3.573213e-7,0.0006581396,0.00006966721,0.9718397,0.00002307497,0.000009702482,0.02567872],"study_design_scores_gemma":[0.0003467835,0.00005492437,0.003123425,0.00005369571,0.001395551,0.00000487737,0.0006486162,0.02088451,0.9724361,0.0005201633,0.0003220109,0.0002093685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8865288,0.003142711,0.1088904,0.0006121509,0.00000221236,0.000207181,0.0000170362,0.00003054539,0.0005689489],"genre_scores_gemma":[0.9978932,0.00002194065,0.001827694,0.0001162151,0.00003609903,0.00004191667,0.000006179683,0.00000475842,0.00005199371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1113643,"threshold_uncertainty_score":0.3147477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0300018999441274,"score_gpt":0.3308549904563537,"score_spread":0.3008530905122264,"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."}}