{"id":"W2763020507","doi":"","title":"Water Footprint of Hydroelectricity: A Case Study of Two Large Canadian Boreal Watersheds","year":2015,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Hydroelectricity; Footprint; Environmental science; Water use; Hydrology (agriculture); Boreal; Water resource management; Geography; Geology; Engineering; Archaeology; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004102603,0.000383738,0.0003362459,0.0009717085,0.003535145,0.00156711,0.001100972,0.0008750812,0.0007441796],"category_scores_gemma":[0.001185917,0.0002227111,0.0003446792,0.003443341,0.001570562,0.0006333713,0.0009239326,0.0005524821,0.0000487513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01445568,"about_ca_system_score_gemma":0.01201177,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9764925,"about_ca_topic_score_gemma":0.9936687,"domain_scores_codex":[0.9995432,0.00005760413,0.00001327142,0.00006579037,0.0001501873,0.0001700718],"domain_scores_gemma":[0.9993824,0.0001349132,0.00008856568,0.00004022207,0.0002282884,0.0001257083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000589732,0.00086312,0.8745257,0.0002494025,0.0002891834,0.01072819,0.01719351,0.02336976,0.009501354,0.004141496,0.004207713,0.05434094],"study_design_scores_gemma":[0.00004428316,0.0001226848,0.9291933,0.00006604467,0.0001223523,0.0008537516,0.04248784,0.01767852,0.001451153,0.000625267,0.007272175,0.00008271763],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965623,0.00008293896,0.0002551829,0.000179393,0.000002108682,0.00003598043,0.0003073813,0.000009682949,0.002564992],"genre_scores_gemma":[0.9980658,0.0001531797,0.0008232369,0.00003585318,0.000001647202,0.00001262749,0.0001972041,0.000004702868,0.0007058175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02350748,"threshold_uncertainty_score":0.1048838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02474353257406349,"score_gpt":0.2415548158629192,"score_spread":0.2168112832888557,"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."}}