{"id":"W2097453622","doi":"10.2166/hydro.2011.044","title":"Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data","year":2011,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":159,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Multivariate adaptive regression splines; Surface runoff; Watershed; Artificial neural network; Multivariate statistics; Mars Exploration Program; Environmental science; Hydrology (agriculture); Regression analysis; Bayesian multivariate linear regression; Computer science; Machine learning; 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":[0.002166641,0.0004646369,0.0005146641,0.000815434,0.0001984351,0.000621895,0.0006227556,0.0004346164,0.000432232],"category_scores_gemma":[0.004962872,0.0002476207,0.0006100958,0.0006964444,0.0001892208,0.0008441303,0.0005324335,0.0005817628,0.00009166814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005553224,"about_ca_system_score_gemma":0.0006282667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01364555,"about_ca_topic_score_gemma":0.009873407,"domain_scores_codex":[0.999476,0.0002925757,0.00004184577,0.00005245153,0.00009846872,0.00003877538],"domain_scores_gemma":[0.9982009,0.001227587,0.0001187103,0.00007041822,0.0003275641,0.00005467457],"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":[0.0004155324,0.000146151,0.007936793,0.00005181225,0.0001421129,0.00004139718,0.00005380838,0.9288085,0.001138692,0.0007431093,0.0001696585,0.06035246],"study_design_scores_gemma":[0.000004928403,0.00003721841,0.0006432185,0.000002160877,0.000008631201,0.000002363497,0.000007103868,0.9989913,0.0001579716,0.000116129,0.00002634395,0.000002564597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8610559,0.0006440592,0.1358414,0.0002306642,0.00005849712,0.00005960037,0.00007804255,0.000264111,0.001767779],"genre_scores_gemma":[0.9757268,0.0002622892,0.02344988,0.00002117359,0.00001406046,0.00002936656,0.00007527703,0.00001373726,0.0004073608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01364555,"threshold_uncertainty_score":0.02713227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1305215527434784,"score_gpt":0.2971876465423763,"score_spread":0.1666660937988979,"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."}}