{"id":"W3095159265","doi":"10.1139/er-2020-0071","title":"Integrating watershed and ecosystem service models to assess best management practice efficiency: guidelines for Lake Erie managers and watershed modellers","year":2020,"lang":"en","type":"article","venue":"Environmental Reviews","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Environment and Climate Change Canada; Ministry of Agriculture, Food and Rural Affairs; University of Toronto","funders":"","keywords":"Watershed; Environmental science; Environmental resource management; Best practice; Nonpoint source pollution; Watershed management; Soil and Water Assessment Tool; Ecosystem services; Surface runoff; Adaptive management; Water resource management; Ecosystem; Ecology; Computer science; Drainage basin; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008738831,0.0004104634,0.0004996151,0.00004327741,0.000362623,0.00007218064,0.0003118984,0.00006974024,0.000119121],"category_scores_gemma":[0.00006216976,0.0003215384,0.00007814689,0.0001479353,0.0000920817,0.0005865954,0.0009525632,0.0001217357,0.0003601611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009850259,"about_ca_system_score_gemma":0.000001404165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000199744,"about_ca_topic_score_gemma":0.0002545865,"domain_scores_codex":[0.9975581,0.0001556235,0.0006293306,0.0009082814,0.0002693258,0.0004793738],"domain_scores_gemma":[0.9992082,0.00006341423,0.0001747192,0.0002841102,0.000003104467,0.0002664561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002408909,0.001707451,0.009453558,0.006373914,0.00219676,0.0003987371,0.08821371,0.3158785,0.01697363,0.001744682,0.1783541,0.376296],"study_design_scores_gemma":[0.0008183739,0.0002658731,0.00007931072,0.0001002816,0.000322642,0.00000867835,0.005301,0.1142195,0.0001305573,0.0001824602,0.8780041,0.0005672207],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4908262,0.007051058,0.2791076,0.1668773,0.0005827381,0.02156676,0.0003020713,0.0003963053,0.03328997],"genre_scores_gemma":[0.7952958,0.01667113,0.1319238,0.05260806,0.0001385603,0.001444546,0.0001938218,0.0001295537,0.001594777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.69965,"threshold_uncertainty_score":0.9999236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1063432250868835,"score_gpt":0.3007982941892144,"score_spread":0.1944550691023309,"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."}}