{"id":"W4384944696","doi":"10.1016/j.jclepro.2023.138197","title":"MarILCA characterization factors for microplastic impacts in life cycle assessment: Physical effects on biota from emissions to aquatic environments","year":2023,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":91,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; ArcelorMittal","keywords":"Life-cycle assessment; Biota; Environmental science; Aquatic environment; Environmental impact assessment; Environmental resource management; Environmental engineering; Ecology; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003249507,0.0001520542,0.0002144104,0.0001761772,0.00009883977,0.00003312596,0.0001089127,0.0000594729,0.00005358675],"category_scores_gemma":[0.001039891,0.0001231647,0.00006714216,0.0002840104,0.00002577464,0.0002083873,0.00004470802,0.0001757593,0.0001417662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002743408,"about_ca_system_score_gemma":0.00002579422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002877935,"about_ca_topic_score_gemma":0.00001140856,"domain_scores_codex":[0.9987367,0.00009676954,0.0003317831,0.000251392,0.0003398103,0.0002435737],"domain_scores_gemma":[0.9990264,0.0003729683,0.000264483,0.0001331567,0.000004827228,0.0001981709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001986863,0.0002113836,0.01019612,0.00001324424,0.0000194908,0.000003270151,0.0005343598,0.02647536,0.953939,0.000002331675,0.001336395,0.007070363],"study_design_scores_gemma":[0.0006147737,0.0005674453,0.9285004,0.0001470125,0.00005198264,0.000003483143,0.00006808518,0.01563136,0.05051189,0.0002725177,0.003449615,0.0001815081],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929926,0.000001841297,0.004413065,0.0006008853,0.001617455,0.0003174114,0.00002898964,0.00001053789,0.00001721894],"genre_scores_gemma":[0.9986766,0.00002262184,0.000267581,0.0000696065,0.0007622214,0.00000639269,0.00006037413,0.00002069645,0.0001138944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9183042,"threshold_uncertainty_score":0.5022512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009859357238024708,"score_gpt":0.2499690412804656,"score_spread":0.2401096840424409,"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."}}