{"id":"W4401332364","doi":"10.1080/02626667.2024.2387155","title":"Estimation of the time of concentration of small watersheds located in Northeastern North America","year":2024,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hydrology (agriculture); Environmental science; Streamflow; Precipitation; Estimation; Regression; Wetland; Physical geography; Geography; Statistics; Ecology; Drainage basin; Mathematics; Geology; Meteorology; Cartography; Biology; Engineering","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":[0.0001886073,0.0001327511,0.0001230822,0.001327345,0.0004335864,0.0004514789,0.0002620896,0.0001302234,0.0007595461],"category_scores_gemma":[0.001081229,0.00006410853,0.0001184084,0.001588738,0.000198965,0.0002462904,0.0001887107,0.0001189718,0.0000964557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002803853,"about_ca_system_score_gemma":0.001801618,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7874811,"about_ca_topic_score_gemma":0.8869272,"domain_scores_codex":[0.9999051,0.00001334365,0.000008088411,0.00002571291,0.00002867613,0.00001898213],"domain_scores_gemma":[0.9990953,0.0002123379,0.0002239397,0.00002214501,0.0003691211,0.00007720887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002739157,0.00002271235,0.9856281,0.00001939854,0.00002078696,0.00009200178,0.0003594101,0.002130918,0.001624726,0.0001206028,0.0004199042,0.009533967],"study_design_scores_gemma":[0.000001354986,0.000006042633,0.9934949,0.000003298335,0.000004897138,0.00001827762,0.0003518944,0.005407277,0.0002167198,0.00002417351,0.0004680775,0.000003142387],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966571,0.00004872652,0.0007017298,0.00003156397,0.000001072653,0.00002137975,0.001517716,0.00002800651,0.0009926889],"genre_scores_gemma":[0.9968281,0.00005869434,0.001211841,0.000004369047,0.000002026524,0.00001896874,0.00132774,0.000003714953,0.000544444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7874811,"threshold_uncertainty_score":0.427541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453952766861891,"score_gpt":0.2244335192469048,"score_spread":0.2098939915782859,"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."}}