{"id":"W4367040796","doi":"10.1016/j.chemolab.2023.104841","title":"Exploring the scores: Procrustes analysis for comprehensive exploration of multivariate data","year":2023,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Data mining; Overfitting; Principal component analysis; Procrustes analysis; Cluster analysis; Hierarchical clustering; Projection pursuit; Preprocessor; Projection (relational algebra); Multivariate statistics; Artificial intelligence; Pattern recognition (psychology); Exploratory data analysis; Visualization; Machine learning; Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02197229,0.005373898,0.003470867,0.009137269,0.00231881,0.005627895,0.003664365,0.001482246,0.01005956],"category_scores_gemma":[0.0651636,0.001601361,0.0062429,0.0109151,0.00252725,0.005465992,0.005716538,0.004795101,0.004309154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006609937,"about_ca_system_score_gemma":0.004866339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004487714,"about_ca_topic_score_gemma":0.005446596,"domain_scores_codex":[0.9825208,0.009620139,0.001337052,0.002626408,0.003282278,0.0006132285],"domain_scores_gemma":[0.9602538,0.03012411,0.001826902,0.004015945,0.003129674,0.0006496725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001407487,0.0007460005,0.02194381,0.001035798,0.003112975,0.0006166804,0.003351677,0.04324264,0.01678959,0.04553285,0.02406881,0.8381518],"study_design_scores_gemma":[0.0001757941,0.0008346277,0.01207201,0.0002497556,0.0009568561,0.0008520142,0.001368666,0.8319811,0.01292581,0.1139913,0.02419956,0.0003924857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01386,0.0002781392,0.9778105,0.0001585836,0.00004214652,0.0001402402,0.000804926,0.006201864,0.0007036834],"genre_scores_gemma":[0.0756612,0.0002594844,0.9172824,0.00007004465,0.00006699297,0.0006012231,0.002167813,0.003170891,0.0007199161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02197229,"threshold_uncertainty_score":0.1162019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3537586377516224,"score_gpt":0.363730012677846,"score_spread":0.009971374926223686,"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."}}