{"id":"W4328104881","doi":"10.1016/j.mex.2023.102143","title":"Analysis of microplastics in the environment: Identification and quantification of trace levels of common types of plastic polymers using pyrolysis-GC/MS","year":2023,"lang":"en","type":"article","venue":"MethodsX","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades; Ministerio de Ciencia e Innovación; Centres de Recerca de Catalunya; Canadian Institute for Advanced Research","keywords":"Microplastics; Polymer; Pyrolysis–gas chromatography–mass spectrometry; Pyrolysis; Polyethylene; Gas chromatography; Chromatography; Mass spectrometry; Environmental chemistry; Gas chromatography–mass spectrometry; Analytical technique; Chemistry; Organic chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004702137,0.0008016113,0.0003498626,0.00140987,0.0003071489,0.0005155327,0.0002973914,0.000590059,0.000639614],"category_scores_gemma":[0.0005159604,0.0002785547,0.0004416683,0.0006030673,0.0003456133,0.0004938737,0.0005521012,0.0004068367,0.0004268037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003075035,"about_ca_system_score_gemma":0.0005363784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009020203,"about_ca_topic_score_gemma":0.002072308,"domain_scores_codex":[0.9993209,0.00006539464,0.00003381701,0.0002372679,0.0002900288,0.00005254777],"domain_scores_gemma":[0.999706,0.00006544798,0.0000765106,0.00002500514,0.000103619,0.00002342959],"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":[0.0001086395,0.00002916932,0.004764275,0.0002552589,0.00005136648,0.0001077656,0.00004390215,0.0005084882,0.9747926,0.00006104134,0.00004007489,0.01923732],"study_design_scores_gemma":[0.000006223993,0.0002235133,0.01906925,0.00004153856,0.0000476515,0.0006073365,0.00007465726,0.003310065,0.9742374,0.000122448,0.002231797,0.00002810916],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8896015,0.005611871,0.09883976,0.0000796959,0.00005740762,0.0002344337,0.002102156,0.0007104834,0.002762765],"genre_scores_gemma":[0.8649226,0.004712631,0.1261199,0.0001593849,0.00002718824,0.0002086256,0.0009278755,0.0001060684,0.002815664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00140987,"threshold_uncertainty_score":0.002486825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04895861806254986,"score_gpt":0.3006998776587564,"score_spread":0.2517412595962065,"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."}}