{"id":"W4283815647","doi":"10.1139/as-2022-0006","title":"Monitoring of microplastic pollution in the Arctic: recent developments in polymer identification, quality assurance and control, and data reporting","year":2022,"lang":"en","type":"article","venue":"Arctic Science","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Framsenteret; Miljøstyrelsen; Agence Nationale de la Recherche; Norges Forskningsråd; Nærings- og Fiskeridepartementet; European Commission; Havforskningsinstituttet","keywords":"Microplastics; Environmental science; Context (archaeology); Quality assurance; Pollution; Arctic; Identification (biology); Sampling (signal processing); The arctic; Contamination; Computer science; Environmental chemistry; Engineering; Ecology; Geography; Oceanography; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005414099,0.00007255838,0.000109969,0.00007742702,0.0003141317,0.00004346279,0.0004084024,0.0000132308,0.00002461182],"category_scores_gemma":[0.002612826,0.00006175973,0.000005618517,0.0007881568,0.0004057909,0.0002775253,0.0004254536,0.0001308408,0.000001770306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001871571,"about_ca_system_score_gemma":0.00007588544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001908041,"about_ca_topic_score_gemma":0.0002704393,"domain_scores_codex":[0.9980944,0.000148221,0.0006131429,0.0004211385,0.0004945229,0.0002285147],"domain_scores_gemma":[0.9988778,0.0003008352,0.000458758,0.0003081031,0.00001499745,0.00003956807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001483484,0.00003937924,0.8832239,0.00001129444,0.000001334499,0.000002234192,0.0007802621,0.0002214614,0.1109965,0.00007773584,0.000008278877,0.004622784],"study_design_scores_gemma":[0.0002476819,0.00001355444,0.9960903,0.00002616721,0.000004650621,0.0000362747,0.0005350677,0.00127591,0.001397367,0.000167007,0.0001334782,0.00007256046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981323,0.0002353714,0.0006829316,0.0004150604,0.0003198141,0.0001448159,0.00001660823,0.000002995828,0.00005011542],"genre_scores_gemma":[0.9992629,0.0000798759,0.0005519614,0.00006948318,0.00001007648,0.00001129425,0.0000021966,0.000002640784,0.000009569363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1128664,"threshold_uncertainty_score":0.3127985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04276210842771404,"score_gpt":0.3037385511740479,"score_spread":0.2609764427463338,"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."}}