{"id":"W2159260443","doi":"10.1186/2193-2697-3-11","title":"Watershed modeling using arc hydro based on DEMs: a case study in Jackpine watershed","year":2014,"lang":"en","type":"article","venue":"ENVIRONMENTAL SYSTEMS RESEARCH","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"China Scholarship Council; University of Regina","keywords":"Watershed; Digital elevation model; Raster graphics; STREAMS; Hydrology (agriculture); Environmental science; Geographic information system; Watershed area; Catchment area; Drainage basin; Remote sensing; Geology; Computer science; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004464962,0.0003580666,0.0002223619,0.0005112438,0.0007926109,0.0006255864,0.0007831346,0.000368594,0.0008877144],"category_scores_gemma":[0.001981583,0.0002194841,0.000277035,0.001446032,0.0004193576,0.0004397159,0.0003415611,0.0002544728,0.000087273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004042817,"about_ca_system_score_gemma":0.002681456,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7333724,"about_ca_topic_score_gemma":0.8041368,"domain_scores_codex":[0.9998299,0.00005934844,0.00001226012,0.00002123857,0.00004964909,0.00002754135],"domain_scores_gemma":[0.9994186,0.0002862089,0.00004605313,0.00006666106,0.0001346271,0.00004776348],"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":[0.0002135423,0.0001896724,0.07922918,0.0001464604,0.00004438662,0.00437638,0.001711823,0.8548161,0.003215506,0.003706707,0.001548125,0.05080219],"study_design_scores_gemma":[0.00004480904,0.0000650089,0.02906609,0.00003019086,0.00003254004,0.0002911922,0.001907978,0.9589112,0.003318213,0.0008031268,0.00549554,0.00003406897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854434,0.00007362238,0.01006601,0.0001604253,0.000005536073,0.00008965778,0.000411326,0.000256634,0.003493448],"genre_scores_gemma":[0.9810843,0.0001698156,0.01696361,0.0000090642,0.000002073347,0.00004034409,0.0003084192,0.00002756084,0.001394776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7333724,"threshold_uncertainty_score":0.5363957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0685714784874184,"score_gpt":0.3141482197940348,"score_spread":0.2455767413066164,"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."}}