{"id":"W2928268417","doi":"10.2478/johh-2018-0041","title":"Regionalizing time of concentration using landscape structural patterns of catchments","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrology and Hydromechanics","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences; Chinese Academy of Sciences","keywords":"Hydrology (agriculture); Contiguity; Drainage basin; Land cover; Environmental science; Land use; Fractal dimension; Scale (ratio); Rangeland; Index (typography); Landscape ecology; Physical geography; Fractal; Soil science; Geography; Mathematics; Ecology; Geology; Cartography; Computer science; Agroforestry","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":[0.001159596,0.0001895426,0.000212708,0.001261368,0.0001275792,0.0007290749,0.0003327978,0.0002212665,0.001304207],"category_scores_gemma":[0.006251127,0.0001054247,0.0003572454,0.0009802921,0.0004018843,0.0006883341,0.0004402058,0.0003046191,0.0001925287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004469355,"about_ca_system_score_gemma":0.0002813753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052735,"about_ca_topic_score_gemma":0.006963818,"domain_scores_codex":[0.999723,0.0001010619,0.00001595003,0.00009003132,0.00003618367,0.00003372236],"domain_scores_gemma":[0.9960803,0.002399184,0.0007201385,0.0002444584,0.0003762032,0.0001796722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001944435,0.00004236936,0.9074686,0.00003556382,0.0001629131,0.0001504476,0.0004875677,0.06047628,0.00610404,0.001811162,0.0003000934,0.02276648],"study_design_scores_gemma":[0.000007756461,0.000147806,0.7576853,0.00001247104,0.0000543316,0.0001193752,0.0003331793,0.2375281,0.001960863,0.001555977,0.0005717947,0.00002302257],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881896,0.00005758261,0.01063367,0.00005030579,0.000002869348,0.00001669934,0.000198399,0.0000660923,0.0007847904],"genre_scores_gemma":[0.9989415,0.0000111775,0.0008584313,0.000001440033,0.000001470314,0.00000492859,0.00008558294,0.000004908851,0.00009065535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01052735,"threshold_uncertainty_score":0.02093214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007962831344412256,"score_gpt":0.2195698930131007,"score_spread":0.2116070616686885,"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."}}