{"id":"W2909420350","doi":"10.3390/geosciences9010046","title":"Modeling the Natural Drainage Network of the Grand River in Southern Ontario: Agriculture May Increase Total Channel Length of Low-Order Streams","year":2019,"lang":"en","type":"article","venue":"Geosciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sinuosity; Hydrology (agriculture); STREAMS; Channel (broadcasting); Digital elevation model; Erosion; Drainage; Watershed; Environmental science; SWAT model; Drainage network; Geology; Sediment; Geomorphology; Remote sensing; Ecology; Structural basin","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001225572,0.00016573,0.0001083875,0.0002761458,0.0004898721,0.0006782642,0.0004333509,0.0002563203,0.001097804],"category_scores_gemma":[0.0008035332,0.0001840567,0.0001881769,0.0004202866,0.0003162897,0.0003299507,0.0001855388,0.0001437841,0.00007792303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006004005,"about_ca_system_score_gemma":0.004688128,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8429201,"about_ca_topic_score_gemma":0.9310848,"domain_scores_codex":[0.9999362,0.000009995418,0.000002490902,0.00002275807,0.000009958813,0.0000184968],"domain_scores_gemma":[0.9997941,0.00008252616,0.00003256714,0.00001060461,0.0000566037,0.00002368381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007636385,0.00005170763,0.288672,0.0000520115,0.00003889683,0.0002022948,0.0002904708,0.6961994,0.002305588,0.001341114,0.0009153011,0.009854793],"study_design_scores_gemma":[0.00002276065,0.00002295643,0.1250518,0.00001090516,0.00002685484,0.00003353732,0.0003015342,0.8719351,0.0004637215,0.0004321597,0.001687941,0.00001070658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961469,0.00003014502,0.0012437,0.00006240948,0.000001895544,0.00001484605,0.0003960709,0.00003829764,0.002065677],"genre_scores_gemma":[0.9977629,0.00003571686,0.0009164683,0.000004900413,8.733411e-7,0.00000814805,0.0002008783,0.000005410618,0.001064786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1570799,"threshold_uncertainty_score":0.31601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004267416653089522,"score_gpt":0.1745293679759685,"score_spread":0.170261951322879,"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."}}