{"id":"W4399388976","doi":"10.3390/jmse12060953","title":"A Laboratory Dataset on Transport and Deposition of Spherical and Cylindrical Large Microplastics for Validation of Numerical Models","year":2024,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microplastics; Flume; Deposition (geology); Environmental science; Particle (ecology); Flow (mathematics); Marine engineering; Benchmark (surveying); Channel (broadcasting); Mechanics; Geology; Computer science; Engineering; Physics; Oceanography; Sediment","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":[0.0006690329,0.0007639049,0.0009791082,0.001675114,0.0007034269,0.000883048,0.001016227,0.001761425,0.002412686],"category_scores_gemma":[0.001366251,0.0002696858,0.001110105,0.001805604,0.0004880997,0.001000532,0.0006206612,0.000672482,0.001904373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007939246,"about_ca_system_score_gemma":0.0009384204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01110461,"about_ca_topic_score_gemma":0.01575102,"domain_scores_codex":[0.9995376,0.00003972144,0.00004994154,0.0001513236,0.0001412925,0.00008020854],"domain_scores_gemma":[0.9988544,0.0002903605,0.0001185671,0.000287113,0.0003748516,0.00007463813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001947265,0.002946597,0.2410986,0.003663938,0.0007488303,0.003958008,0.0006891568,0.4172882,0.1128856,0.004389802,0.09159762,0.1187865],"study_design_scores_gemma":[0.0002628263,0.0009735622,0.3091382,0.0004013708,0.0002001888,0.001192875,0.001135071,0.5455378,0.05880523,0.003047552,0.07898151,0.0003238014],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.7396339,0.001247297,0.01510912,0.0004274381,0.0002349986,0.0003099137,0.2298686,0.002869955,0.01029873],"genre_scores_gemma":[0.6849657,0.0007083555,0.02474779,0.0001316062,0.00005690783,0.0003985775,0.2861345,0.0002466979,0.002610016],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01110461,"threshold_uncertainty_score":0.02207994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006607786736111514,"score_gpt":0.2074612678585211,"score_spread":0.2008534811224096,"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."}}