{"id":"W4375842820","doi":"10.1007/s10661-023-11277-8","title":"Spatial distribution of microplastics in a large watershed: a case study of the Ottawa River watershed","year":2023,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Microplastics; Watershed; Tributary; Environmental science; Spatial distribution; Pollution; Hydrology (agriculture); Sampling (signal processing); Spatial ecology; Channel (broadcasting); Geography; Ecology; Geology; Remote sensing; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002317133,0.0001242337,0.0001493089,0.00002951038,0.0001159562,0.000008586798,0.00008812324,0.00004273346,0.00003842731],"category_scores_gemma":[0.0000081919,0.00009194001,0.0000308804,0.0001251087,0.0001230074,0.00004983108,0.0003186681,0.0001289385,0.000009082498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001664833,"about_ca_system_score_gemma":0.000004770351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001967762,"about_ca_topic_score_gemma":0.0001365503,"domain_scores_codex":[0.9989265,0.00008475431,0.0002835963,0.0002146765,0.0002669862,0.0002234779],"domain_scores_gemma":[0.9996361,0.00005467353,0.0001041973,0.0001575675,0.000001194129,0.00004628066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001097741,0.0004504902,0.9481782,0.00001079831,0.00001294266,0.00006762586,0.001628025,0.001369661,0.04720991,0.000002401598,0.00002682511,0.001032131],"study_design_scores_gemma":[0.0008920947,0.0001860305,0.9855352,0.00002164639,0.0000283668,0.00003185458,0.003558555,0.002062136,0.007395704,0.00001077518,0.0001782066,0.0000994142],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990594,0.000005221237,0.0002782379,0.00001165056,0.0002526814,0.0002456746,0.0001155679,0.000007313136,0.00002419974],"genre_scores_gemma":[0.999786,0.00001760833,0.00009045234,0.000001130006,0.00002374281,0.00001238671,0.00001863181,0.000008183862,0.00004193153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03981421,"threshold_uncertainty_score":0.3749205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009182796806605947,"score_gpt":0.2363583107706103,"score_spread":0.2271755139640043,"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."}}