{"id":"W2947734915","doi":"10.1016/j.ecolind.2019.03.050","title":"Examining lag time using the landscape, pedoscape and lithoscape metrics of catchments","year":2019,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Chinese Academy of Sciences; Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences; U.S. Department of Agriculture","keywords":"Land cover; Hydrology (agriculture); Drainage basin; Environmental science; Lag; Land use; Fractal dimension; Physical geography; Structural basin; Fractal; Geology; Geography; Cartography; Mathematics; Ecology; Geomorphology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001436369,0.0002537973,0.0002039778,0.001870475,0.0002519056,0.001421724,0.0002697876,0.0002769573,0.001820582],"category_scores_gemma":[0.008621504,0.00008946708,0.0003155795,0.003099815,0.0001984675,0.001284014,0.0006710418,0.0003778065,0.0001905148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000608679,"about_ca_system_score_gemma":0.0007590225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0362709,"about_ca_topic_score_gemma":0.06389191,"domain_scores_codex":[0.9995987,0.00009693523,0.00004002386,0.00009075861,0.00008050903,0.00009310531],"domain_scores_gemma":[0.9948099,0.002790035,0.00107367,0.0002455012,0.0006685374,0.0004123584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001437677,0.00002725879,0.9869477,0.00001371031,0.00007617714,0.00004168184,0.0002889863,0.003330228,0.0005719839,0.0005765442,0.0001942838,0.007787813],"study_design_scores_gemma":[0.000009007562,0.0001148509,0.9639264,0.00001136487,0.00005394342,0.00007110326,0.001117056,0.03185023,0.0006310827,0.0009279306,0.00127214,0.00001493131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968204,0.0000531385,0.001968536,0.00002563002,0.000004822144,0.000004872945,0.0005259021,0.0000242307,0.0005722399],"genre_scores_gemma":[0.9987077,0.00001755358,0.000603302,0.000002824391,0.000002129134,0.000004261519,0.0003981362,0.000006596028,0.0002574884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0362709,"threshold_uncertainty_score":0.07211959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736679510206936,"score_gpt":0.2306979945951469,"score_spread":0.2133311994930776,"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."}}