{"id":"W4297675047","doi":"10.1016/b978-0-323-99908-3.00020-8","title":"Quantitative and qualitative identification, characterization, and analysis of microplastics and nanoplastics in water","year":2022,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Microplastics; Characterization (materials science); Elemental analysis; Nanoparticle tracking analysis; Quantitative analysis (chemistry); Microscopy; Transmission electron microscopy; Fourier transform infrared spectroscopy; Qualitative analysis; Nanotechnology; Materials science; Chemistry; Analytical Chemistry (journal); Biological system; Chromatography; Environmental chemistry; Chemical engineering; Optics; Engineering; Organic chemistry; Physics","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":[0.000358112,0.0006950758,0.0004970505,0.001532163,0.0003886046,0.00152598,0.0006487803,0.0007028707,0.006650324],"category_scores_gemma":[0.0003399114,0.0003790643,0.0004163171,0.001319718,0.0009482024,0.001536894,0.0005617344,0.0008658164,0.002578304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000563501,"about_ca_system_score_gemma":0.0005267669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009037916,"about_ca_topic_score_gemma":0.00199245,"domain_scores_codex":[0.9996419,0.00002615771,0.00001529615,0.00006894295,0.0002282675,0.00001938857],"domain_scores_gemma":[0.9997694,0.0001196052,0.00001840891,0.00002045091,0.0000625336,0.000009613856],"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":[0.00006374248,0.0001218634,0.0005989129,0.001693941,0.0000183678,0.0001612764,0.0002483318,0.002231166,0.6457684,0.01221715,0.006134875,0.3307419],"study_design_scores_gemma":[0.00000699927,0.0002293315,0.007388738,0.0003783932,0.00004555943,0.001149609,0.0004140562,0.007320144,0.7055234,0.03098135,0.2465011,0.00006138994],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0994737,0.1808669,0.4959568,0.002177869,0.001136898,0.0005517335,0.004643973,0.001602547,0.2135895],"genre_scores_gemma":[0.198854,0.1142693,0.2636156,0.001398619,0.0004545569,0.0006106284,0.003067418,0.0008584976,0.4168714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006650324,"threshold_uncertainty_score":0.02224755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01337236112399176,"score_gpt":0.2430308948651592,"score_spread":0.2296585337411674,"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."}}