{"id":"W1507824081","doi":"10.1002/hyp.10515","title":"A preliminary assessment of water partitioning and ecohydrological coupling in northern headwaters using stable isotopes and conceptual runoff models","year":2015,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; Environment and Climate Change Canada; University of Toronto; McMaster University; Trent University","funders":"Environment Canada; Kempe Foundation; Natural Sciences and Engineering Research Council of Canada; Svensk Kärnbränslehantering; Svenska Forskningsrådet Formas; Garfield Weston Foundation","keywords":"Hydrology (agriculture); Surface runoff; Snowmelt; Environmental science; Drainage basin; Surface water; Water storage; Potential evaporation; Riparian zone; Soil water; Precipitation; Groundwater; Snow; Water content; Geology; Soil science; Ecology; Habitat","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":[0.003435755,0.0004833397,0.000649819,0.0007754043,0.0002876332,0.0009231485,0.0007114344,0.0002903462,0.0009725695],"category_scores_gemma":[0.00266187,0.0003936081,0.001866712,0.001358735,0.000262705,0.0006860194,0.0004419025,0.0002704776,0.00007838786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293573,"about_ca_system_score_gemma":0.001181299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08518847,"about_ca_topic_score_gemma":0.0610811,"domain_scores_codex":[0.9992614,0.0005049771,0.00002485499,0.0001262855,0.00005137471,0.00003100188],"domain_scores_gemma":[0.9983903,0.001204971,0.0001103878,0.0001167951,0.0001208294,0.00005682257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007599895,0.0001760983,0.4666164,0.0004980857,0.006474337,0.0001647824,0.0009116491,0.4631619,0.006865982,0.003587642,0.0005225414,0.05026051],"study_design_scores_gemma":[0.0001422648,0.0004485508,0.2464872,0.00009929111,0.002590739,0.00004479756,0.000794352,0.7400466,0.002265256,0.003687552,0.003327831,0.00006556367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912377,0.0005310675,0.006882915,0.0001004047,0.000004062314,0.00002059157,0.0004313767,0.00007762421,0.0007142905],"genre_scores_gemma":[0.9946257,0.0001852041,0.004642993,0.00001350247,0.000004121578,0.00001952899,0.0002907286,0.00001476503,0.0002033177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08518847,"threshold_uncertainty_score":0.1693853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05283301749894518,"score_gpt":0.2670134545298788,"score_spread":0.2141804370309337,"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."}}