{"id":"W4284882234","doi":"10.1139/as-2022-0011","title":"Future monitoring of litter and microplastics in the Arctic—challenges, opportunities, and strategies","year":2022,"lang":"en","type":"article","venue":"Arctic Science","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; University of Toronto; Carleton University; Environment and Climate Change Canada","funders":"Nærings- og Fiskeridepartementet; Miljøstyrelsen; Naturvårdsverket; Havforskningsinstituttet","keywords":"Microplastics; Litter; Marine debris; Arctic; Environmental science; The arctic; Fishery; Environmental resource management; Oceanography; Environmental planning; Ecology; Biology; Geology","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":[0.0007690848,0.00007738284,0.00007495531,0.00005519357,0.0002983003,0.00005049907,0.0002448521,0.00001532341,0.00009848818],"category_scores_gemma":[0.00005617315,0.0000592102,0.000008612735,0.000242451,0.0007060426,0.0001810617,0.0003121118,0.0001513414,0.000001448073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006800693,"about_ca_system_score_gemma":0.0000466979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003071582,"about_ca_topic_score_gemma":0.00005129685,"domain_scores_codex":[0.9990147,0.0000580442,0.0001324878,0.0002195005,0.0003650777,0.0002101342],"domain_scores_gemma":[0.9995537,0.0001863037,0.00005633318,0.0001440549,0.000008123642,0.00005141732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001368644,0.0005158524,0.609284,0.0003567212,0.00002410473,0.00019245,0.06922869,0.004844517,0.1342555,0.101625,0.0004431616,0.07909305],"study_design_scores_gemma":[0.0002840034,0.0002269955,0.9409251,0.00003399115,0.00001577326,0.0002610725,0.0447778,0.001185362,0.0003140852,0.003410901,0.008370161,0.0001947578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972156,0.0006112684,0.00006404323,0.0009376658,0.0002651633,0.00007953792,0.000008365885,0.000003807433,0.0008146036],"genre_scores_gemma":[0.998477,0.0007651719,0.0006254976,0.00006764845,0.00003294427,0.000007332963,4.271343e-7,0.000003148546,0.0000208303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.331641,"threshold_uncertainty_score":0.2601444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03281741371484326,"score_gpt":0.2296855339065875,"score_spread":0.1968681201917443,"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."}}