{"id":"W2048036286","doi":"10.2478/sggw-2013-0007","title":"Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India","year":2013,"lang":"en","type":"article","venue":"Annals of Warsaw University of Life Sciences – SGGW Land Reclamation","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surface runoff; Base flow; Watershed; Support vector machine; Environmental science; Hydrology (agriculture); Water resources; Flow (mathematics); Geography; Mathematics; Computer science; Drainage basin; Engineering; Ecology; Machine learning; Cartography; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002745384,0.0002559513,0.0002599006,0.0006158046,0.0002829862,0.0005178588,0.0004530047,0.0002576006,0.0003363247],"category_scores_gemma":[0.0008817603,0.0001465724,0.0003595378,0.0007861101,0.0002071723,0.0002747128,0.0002355489,0.0003188313,0.00008867724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006752827,"about_ca_system_score_gemma":0.0006051328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0541435,"about_ca_topic_score_gemma":0.05813105,"domain_scores_codex":[0.9998615,0.00003960769,0.00001402354,0.00002548602,0.00003212077,0.00002729613],"domain_scores_gemma":[0.9994504,0.0003258795,0.00005932003,0.00003306938,0.00009379721,0.00003759799],"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":[0.0004332078,0.0006610836,0.4236131,0.0001290953,0.000207379,0.001288499,0.0007418238,0.498532,0.01250099,0.0005403078,0.001045272,0.06030729],"study_design_scores_gemma":[0.00002098905,0.0001597143,0.1895983,0.000006884142,0.00003682527,0.00006172644,0.0005901578,0.8061949,0.002854364,0.0001929992,0.0002624308,0.00002068683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989871,0.000007883596,0.0007142053,0.00002487377,0.000001422616,0.000004623941,0.00007203858,0.00003174179,0.0001561748],"genre_scores_gemma":[0.9989823,0.00001113106,0.0007014283,0.000002564454,0.000001157563,0.000005910455,0.0001743795,0.00000163017,0.0001196067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0541435,"threshold_uncertainty_score":0.1076567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06623861588025744,"score_gpt":0.2624961674130786,"score_spread":0.1962575515328212,"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."}}