{"id":"W2000095052","doi":"10.1007/s10044-009-0170-1","title":"Representation and classification of high-dimensional biomedical spectral data","year":2009,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; National Research Council Institute for Biodiagnostics; University of Alberta","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Dimensionality reduction; Principal component analysis; Computer science; Linear subspace; Perceptron; Classifier (UML); Feature vector; k-nearest neighbors algorithm; Random subspace method; Support vector machine; Subspace topology; Artificial neural network; 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":[0.0006044031,0.0004378986,0.0005456283,0.002318563,0.0002556464,0.001524142,0.0006075175,0.0006307609,0.001188591],"category_scores_gemma":[0.001895018,0.0001425952,0.0005859963,0.002200041,0.0005442711,0.001266473,0.0007365647,0.0007324719,0.0006351029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858592,"about_ca_system_score_gemma":0.0004439823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005346623,"about_ca_topic_score_gemma":0.0004926822,"domain_scores_codex":[0.9995447,0.00008725202,0.00004091706,0.00007187018,0.0002108743,0.0000444531],"domain_scores_gemma":[0.9993677,0.0002070323,0.0001123217,0.0001392636,0.0001425357,0.0000312809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001525157,0.0002198266,0.001827851,0.0004070121,0.0000501512,0.0001965822,0.0002284551,0.03545289,0.08245486,0.01666163,0.004102425,0.8582458],"study_design_scores_gemma":[0.00001637925,0.0001611657,0.00634609,0.00007276469,0.00006532894,0.0008370099,0.0002528664,0.9026588,0.03097561,0.050754,0.007802713,0.00005732736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0562464,0.0009004222,0.9392321,0.000563133,0.00008691629,0.00005244331,0.0007588103,0.0007937761,0.001366017],"genre_scores_gemma":[0.515196,0.002069848,0.4776025,0.0001655883,0.0001920752,0.0001751089,0.002372287,0.00009846681,0.002128098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002318563,"threshold_uncertainty_score":0.003976166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03573797126209471,"score_gpt":0.332521243984583,"score_spread":0.2967832727224883,"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."}}