{"id":"W4378966484","doi":"10.1017/plc.2023.2.pr4","title":"Decision: Monitoring to conservation: The science–policy nexus of plastics and seabirds — R0/PR4","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Plastic pollution; Seabird; Nexus (standard); Environmental planning; Citizen science; Threatened species; Porpoise; Population; Marine conservation; Marine pollution; Environmental science; Environmental resource management; Environmental protection; Pollution; Business; Ecology; Engineering; Biology; Environmental health; Computer science; Habitat","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":[0.02595328,0.0007659873,0.001260964,0.002462883,0.003453583,0.01234499,0.003638432,0.01982044,0.05923714],"category_scores_gemma":[0.08818724,0.0006408799,0.001498841,0.001683554,0.00326886,0.01012741,0.005927441,0.009469857,0.02452051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004844449,"about_ca_system_score_gemma":0.04258992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009805566,"about_ca_topic_score_gemma":0.01147411,"domain_scores_codex":[0.9801467,0.005987061,0.00155327,0.001418527,0.00893568,0.001958763],"domain_scores_gemma":[0.9455792,0.01572889,0.004013372,0.001969815,0.02435778,0.008350943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008908669,0.00006536513,0.0006065895,0.001077109,0.00005324771,0.0003253579,0.000234228,0.0002687123,0.0006226561,0.02745981,0.8870631,0.08213485],"study_design_scores_gemma":[0.00003606396,0.00003262146,0.0004584496,0.0006902694,0.0000232743,0.00005067625,0.0001587415,0.0001604269,0.0002804961,0.008207959,0.9898774,0.00002360594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.004104336,0.02137769,0.004771261,0.7027953,0.05100743,0.001996608,0.002087586,0.0006173732,0.2112424],"genre_scores_gemma":[0.07245356,0.04357224,0.0208067,0.5559832,0.03110754,0.002488052,0.002435454,0.000926085,0.2702272],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05923714,"threshold_uncertainty_score":0.198168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03697803563030876,"score_gpt":0.3112624040838333,"score_spread":0.2742843684535245,"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."}}