{"id":"W4293577525","doi":"10.1016/j.talanta.2022.123861","title":"Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis","year":2022,"lang":"en","type":"article","venue":"Talanta","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Chemistry; Molecular descriptor; Retention time; Support vector machine; Chromatography; Outlier; Random forest; False positive paradox; Biological system; Chemometrics; Statistics; Artificial intelligence; Quantitative structure–activity relationship; Computer science; 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":[0.001387353,0.001071993,0.0006409599,0.0005297761,0.00024799,0.0008075875,0.001308955,0.0007465012,0.0009125611],"category_scores_gemma":[0.00249518,0.0004681161,0.001061065,0.0004244333,0.0002030204,0.001554505,0.0005257489,0.000914171,0.0005732551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006879416,"about_ca_system_score_gemma":0.001186394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004252983,"about_ca_topic_score_gemma":0.004703028,"domain_scores_codex":[0.9996618,0.00009371796,0.00001873414,0.0000765011,0.0001169693,0.00003220139],"domain_scores_gemma":[0.9991876,0.0003908602,0.0001089402,0.00006815972,0.0002270765,0.00001726583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002658411,0.0006720255,0.008563877,0.0005736764,0.0003696169,0.0001759901,0.0001263672,0.7127088,0.1397821,0.0115162,0.002578784,0.1226669],"study_design_scores_gemma":[0.000008052442,0.00007595332,0.0005140055,0.000005930272,0.00002480784,0.00003089541,0.00001040063,0.9814349,0.01601862,0.001183106,0.0006797801,0.00001353725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07912888,0.0005691673,0.91689,0.0001632685,0.00002615569,0.0001291444,0.0006476344,0.001023474,0.001422383],"genre_scores_gemma":[0.7108496,0.00115816,0.2811303,0.0002049832,0.00002627262,0.0005999191,0.001566511,0.0002951468,0.004169084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004252983,"threshold_uncertainty_score":0.008456409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03680184619489122,"score_gpt":0.2482541106247118,"score_spread":0.2114522644298206,"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."}}