{"id":"W3015416021","doi":"10.1177/0003702820922900","title":"Towards Raman Automation for Microplastics: Developing Strategies for Particle Adhesion and Filter Subsampling","year":2020,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microplastics; Particle (ecology); Filter (signal processing); Particle filter; Automation; Raman spectroscopy; Process engineering; Computer science; Nanotechnology; Environmental science; Adhesive; Surface-enhanced Raman spectroscopy; Materials science; Biological system; Chemistry; Physics; Environmental chemistry; Optics; Mechanical engineering; Engineering; Biology; Computer vision; Oceanography; Geology","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":[0.007128927,0.00157538,0.0009926226,0.001642894,0.0009883766,0.002345681,0.001538156,0.001513368,0.001531098],"category_scores_gemma":[0.006700771,0.0009147183,0.001122606,0.000963657,0.001892675,0.002351156,0.001906094,0.002515787,0.001415607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008494706,"about_ca_system_score_gemma":0.001448675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002657997,"about_ca_topic_score_gemma":0.005471671,"domain_scores_codex":[0.9962128,0.00087809,0.0002545719,0.000970433,0.001407281,0.0002767619],"domain_scores_gemma":[0.9940013,0.002147214,0.0009058328,0.001079179,0.001662407,0.0002039935],"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":[0.0000855222,0.000173321,0.002087764,0.0005800057,0.00005879442,0.0001327067,0.0005151106,0.003179277,0.9169649,0.004020276,0.0005971019,0.07160521],"study_design_scores_gemma":[0.00002125473,0.0005399061,0.005697509,0.0001358836,0.00008240538,0.0004451374,0.000419377,0.02188906,0.9436947,0.00441754,0.02252927,0.0001278112],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0700022,0.002477741,0.9227272,0.0007450767,0.0001959188,0.0007728187,0.0001559204,0.0008626794,0.002060567],"genre_scores_gemma":[0.1466363,0.002323833,0.847133,0.0004117297,0.00008696114,0.000637958,0.0002903682,0.0002793309,0.002200476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007128927,"threshold_uncertainty_score":0.03770179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317735372338785,"score_gpt":0.2490791305202613,"score_spread":0.2259017767968734,"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."}}