{"id":"W3082200404","doi":"10.1002/cem.3299","title":"Partial least squares discrimination applied to a few samples dataset: A case for predicting the presence of pesticide in lettuce","year":2020,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Pesticide Residue Analysis and Safety","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Partial least squares regression; Linear discriminant analysis; Statistics; Reliability (semiconductor); Mathematics; Computer science; Wilcoxon signed-rank test; Pattern recognition (psychology); Artificial intelligence","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":[0.001961561,0.0007109769,0.0007536229,0.0007919411,0.0006764124,0.001043213,0.0005972759,0.00119487,0.0004117221],"category_scores_gemma":[0.002721221,0.0001537365,0.00113272,0.0009461314,0.0004880999,0.0003885016,0.0004237654,0.0008371331,0.0002092756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004604634,"about_ca_system_score_gemma":0.0005419042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005522347,"about_ca_topic_score_gemma":0.005997598,"domain_scores_codex":[0.9991635,0.0002783594,0.00006015302,0.0002371276,0.0001946823,0.00006629623],"domain_scores_gemma":[0.9983961,0.0009047181,0.0001162267,0.0002028156,0.0003144597,0.00006563294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002465158,0.002383986,0.2384963,0.0008718519,0.0006866985,0.009560866,0.001100103,0.3471256,0.1348123,0.00155153,0.003855481,0.2570902],"study_design_scores_gemma":[0.00004726323,0.0007812991,0.09625943,0.00004917571,0.0001487753,0.000889199,0.0007170499,0.8634822,0.03290313,0.002141612,0.002493578,0.00008729193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666378,0.0003584989,0.03132658,0.0004230521,0.0000515691,0.00004980192,0.0004405491,0.0001689179,0.0005431556],"genre_scores_gemma":[0.9743398,0.0001168719,0.02443392,0.00006880132,0.00002108585,0.0000334626,0.0005562163,0.00001545596,0.0004142904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005522347,"threshold_uncertainty_score":0.01098037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07200196356014445,"score_gpt":0.284736908308411,"score_spread":0.2127349447482666,"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."}}