{"id":"W4385729168","doi":"10.1002/rra.4201","title":"Assessing channel geometry in response to land use disturbance in a low‐relief, glacially conditioned setting","year":2023,"lang":"en","type":"article","venue":"River Research and Applications","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Hydrology (agriculture); Channel (broadcasting); Flood myth; Overbank; Drainage density; Surface runoff; Geology; Aggradation; Fluvial; Geomorphology; Geography; Geotechnical engineering; Structural basin","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001745745,0.0001385567,0.0001349804,0.0006042767,0.0004853977,0.0006119051,0.0002467696,0.0001671603,0.000643253],"category_scores_gemma":[0.0009415761,0.000112685,0.0001140382,0.0008193232,0.0005565637,0.000143643,0.0003219334,0.0001125791,0.00009299958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002169746,"about_ca_system_score_gemma":0.001179345,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5732443,"about_ca_topic_score_gemma":0.8414803,"domain_scores_codex":[0.9998255,0.00002316434,0.000008668852,0.00004548873,0.00003680008,0.00006027232],"domain_scores_gemma":[0.9995239,0.00005544295,0.0001364065,0.00003004852,0.000123104,0.0001310849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003973276,0.000007587737,0.9973139,0.000003034697,0.00001116591,0.00003833896,0.000196172,0.0003718955,0.0006815467,0.00001500576,0.00005359077,0.001267888],"study_design_scores_gemma":[4.742889e-7,0.000007957103,0.9994642,7.607401e-7,0.000001780679,0.00001111434,0.0002427796,0.0001909253,0.00003598544,0.000005119962,0.0000377471,0.000001039146],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996266,0.000009203489,0.00004323879,0.000004104797,3.530185e-7,0.000002807436,0.0001676226,0.00000176446,0.0001443654],"genre_scores_gemma":[0.9997728,0.000007225027,0.00003824274,0.000002255056,2.78413e-7,0.000001517859,0.000109818,5.453193e-7,0.00006731535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5732443,"threshold_uncertainty_score":0.8585378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03831258568926203,"score_gpt":0.3438583688905688,"score_spread":0.3055457832013067,"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."}}