{"id":"W2586854690","doi":"","title":"The Use of Inductive and Deductive Reasoning to Model Snowmelt Runoff from Northern Mountain Catchments","year":2006,"lang":"en","type":"article","venue":"ScholarsArchive  (Brigham Young University)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snowmelt; Deductive reasoning; Inductive reasoning; Surface runoff; Snow; Hydrology (agriculture); Environmental science; Geography; Geology; Computer science; Meteorology; Geotechnical engineering; Artificial intelligence; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005868767,0.0003734083,0.0002430912,0.0004564042,0.0004782172,0.00067695,0.0006734877,0.0004598539,0.0009893704],"category_scores_gemma":[0.00164528,0.0002519995,0.0004794782,0.0005177201,0.0005004952,0.0004931848,0.0003404142,0.0002924376,0.00008380195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001715506,"about_ca_system_score_gemma":0.001118639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06489399,"about_ca_topic_score_gemma":0.05529811,"domain_scores_codex":[0.9998739,0.00005544348,0.00001058795,0.00002056924,0.00002132877,0.00001818063],"domain_scores_gemma":[0.9994241,0.000451281,0.00004336332,0.00002213249,0.00004262594,0.00001651698],"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":[0.00001740769,0.00002856097,0.003140763,0.0000182362,0.00001008718,0.00008864113,0.0001359777,0.9897503,0.000542199,0.0009465266,0.00006237063,0.00525888],"study_design_scores_gemma":[0.000005739309,0.00001111751,0.0005779333,0.000001775239,0.000003630987,0.000005240247,0.00002614567,0.9981409,0.0003420398,0.0007181413,0.0001651613,0.000002097766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8793879,0.00006681485,0.1151147,0.000133293,0.000007028419,0.0001474513,0.0005996194,0.0007464153,0.003796642],"genre_scores_gemma":[0.9703491,0.00003965057,0.02847654,0.00001479582,0.000004705922,0.00009329672,0.0003185672,0.00001898923,0.0006843669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06489399,"threshold_uncertainty_score":0.1290326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125156614452446,"score_gpt":0.1897563835746518,"score_spread":0.1772407221294072,"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."}}