{"id":"W4309191750","doi":"10.1016/j.scitotenv.2022.160038","title":"Generating environmental sampling and testing data for micro- and nanoplastics for use in life cycle impact assessment","year":2022,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Bio-Based Industries Joint Undertaking; Norges Forskningsråd; HORIZON EUROPE Framework Programme; Hydro-Québec; ArcelorMittal; Università degli Studi di Napoli Parthenope; European Commission; Università degli Studi di Napoli Federico II","keywords":"Life-cycle assessment; Perspective (graphical); Environmental impact assessment; Computer science; Impact assessment; Sampling (signal processing); Environmental science; Data collection; Risk analysis (engineering); Business; Statistics; Artificial intelligence; Ecology; Mathematics; Telecommunications; Production (economics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02919011,0.001228483,0.001013519,0.006364922,0.001296834,0.002249453,0.002867919,0.001456046,0.002027299],"category_scores_gemma":[0.03138056,0.0005590527,0.00163068,0.004607553,0.001172896,0.003124798,0.002877858,0.0011213,0.001371307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00219667,"about_ca_system_score_gemma":0.003956223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005230494,"about_ca_topic_score_gemma":0.008363665,"domain_scores_codex":[0.9744182,0.01055433,0.003224685,0.002161309,0.009200976,0.0004404575],"domain_scores_gemma":[0.947239,0.01805949,0.008314746,0.01390867,0.0121184,0.0003597534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006900379,0.001268269,0.2231444,0.004680248,0.0004321916,0.0004622822,0.001823916,0.05886273,0.1515916,0.007862939,0.004173408,0.545008],"study_design_scores_gemma":[0.0001806972,0.004521159,0.1858296,0.001825589,0.0007088191,0.0009726096,0.0046413,0.06539956,0.6300225,0.01846614,0.08696178,0.000470238],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.174355,0.00118873,0.7728264,0.0009502422,0.0001513416,0.006344909,0.02574789,0.001931139,0.0165045],"genre_scores_gemma":[0.3390221,0.002313172,0.6303536,0.0004559003,0.00008075002,0.007601774,0.0179313,0.000352513,0.001888867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02919011,"threshold_uncertainty_score":0.1543739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03962090431031683,"score_gpt":0.2614196032947111,"score_spread":0.2217986989843942,"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."}}