{"id":"W4303684570","doi":"10.1016/j.chemosphere.2022.136772","title":"Representative subsampling methods for the chemical identification of microplastic particles in environmental samples","year":2022,"lang":"en","type":"article","venue":"Chemosphere","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Comparability; Representativeness heuristic; Microplastics; Identification (biology); Diversity (politics); Sampling (signal processing); Particle (ecology); Environmental science; Statistics; Computer science; Ecology; Mathematics; Biology","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":[0.001877682,0.0008255882,0.0006184098,0.002312909,0.0013071,0.0006180498,0.0007970948,0.0007657657,0.001864853],"category_scores_gemma":[0.001961296,0.0003987945,0.0007182939,0.001162427,0.0004077132,0.0003241607,0.0006500856,0.0005183492,0.0009648044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000388509,"about_ca_system_score_gemma":0.0006500252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003753864,"about_ca_topic_score_gemma":0.01424646,"domain_scores_codex":[0.9981152,0.0003430587,0.0001642653,0.0004468306,0.0008111851,0.0001193714],"domain_scores_gemma":[0.9987932,0.0002236637,0.0001353438,0.0002218501,0.0005411022,0.00008463714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003118176,0.0001732508,0.004477433,0.0002695043,0.00008598577,0.00008034514,0.0002053692,0.0003753024,0.9493509,0.0002814878,0.0008175642,0.04357103],"study_design_scores_gemma":[0.00002933169,0.0008612046,0.04664709,0.00006731351,0.0003085232,0.0008268534,0.0002438898,0.004153065,0.9258249,0.0003469438,0.02062804,0.00006276571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4410387,0.008516602,0.5345685,0.0002509402,0.0005257337,0.002103727,0.00461809,0.00154086,0.00683682],"genre_scores_gemma":[0.3372786,0.005682492,0.6387542,0.0004422911,0.0001778137,0.002720443,0.006850093,0.0004350488,0.007658966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003753864,"threshold_uncertainty_score":0.009930253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02358250125434973,"score_gpt":0.2892046507955925,"score_spread":0.2656221495412428,"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."}}