{"id":"W4386209796","doi":"10.1021/acs.est.3c01885","title":"Cleaning Up without Messing Up: Maximizing the Benefits of Plastic Clean-Up Technologies through New Regulatory Approaches","year":2023,"lang":"en","type":"review","venue":"Environmental Science & Technology","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Toronto","funders":"H2020 Food; National Institute of Environmental Health Sciences; Horizon 2020 Framework Programme; Natural Sciences and Engineering Research Council of Canada; Vlaamse regering; Norsk Institutt for Vannforskning; Fonds Wetenschappelijk Onderzoek; Norges Forskningsråd; National Institutes of Health; Alexander Graham Bell Association for the Deaf and Hard of Hearing","keywords":"Business; Plastic pollution; Outreach; Emerging technologies; Environmental economics; Pollution; Environmental planning; Environmental resource management; Environmental science; Computer science; Economics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008311505,0.0007313045,0.001190858,0.0005618641,0.001061005,0.00009015136,0.0027291,0.0007803945,0.000194972],"category_scores_gemma":[0.0004446892,0.0005288541,0.0002917531,0.002715146,0.007890125,0.0004173567,0.003196399,0.001072734,0.0006525629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009850424,"about_ca_system_score_gemma":0.0001629976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006062738,"about_ca_topic_score_gemma":0.00003116519,"domain_scores_codex":[0.9953511,0.00007371647,0.0009653824,0.001428561,0.001020385,0.001160828],"domain_scores_gemma":[0.997392,0.0002926319,0.0009735617,0.001225996,0.000003487412,0.0001123524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007313321,0.0000365344,0.000300501,0.0002644336,0.00005081461,0.000003505945,0.0003399974,0.0008280767,0.0006784665,0.002063549,0.0003049498,0.9951218],"study_design_scores_gemma":[0.001568254,0.0007720799,0.002686462,0.01365655,0.002310367,0.0008029614,0.01692551,0.002945722,0.01007316,0.009975386,0.933705,0.004578544],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01983629,0.9664422,0.004957427,0.0002710558,0.003667585,0.002148481,0.0001763028,0.00135965,0.001140973],"genre_scores_gemma":[0.1391267,0.8567488,0.002430757,0.00001836438,0.00008357623,0.00009676205,0.00002794485,0.000147967,0.001319139],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9905433,"threshold_uncertainty_score":0.9997163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06649292069520618,"score_gpt":0.2646354158488009,"score_spread":0.1981424951535947,"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."}}