{"id":"W3094942348","doi":"","title":"Investigating the consistency and reliability of multiple datasets in estimating water balances in major Canadian river basins","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Consistency (knowledge bases); Environmental science; Computer science; Hydrology (agriculture); Geology; Artificial intelligence","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":[0.02066808,0.0006640516,0.0006313866,0.003448979,0.002009718,0.003705423,0.002439416,0.001971571,0.0004146694],"category_scores_gemma":[0.08214261,0.0008066765,0.0009886873,0.004345942,0.001607569,0.001932863,0.001753333,0.001352585,0.0001567194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008446873,"about_ca_system_score_gemma":0.008592282,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8661535,"about_ca_topic_score_gemma":0.893718,"domain_scores_codex":[0.9927928,0.002167682,0.0007901748,0.001715427,0.001926072,0.0006078055],"domain_scores_gemma":[0.9299737,0.04215582,0.005154386,0.006327617,0.01566758,0.0007208404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004561492,0.0001737497,0.9032336,0.0001339354,0.001858962,0.0001322147,0.001064494,0.05595291,0.001661996,0.0007373925,0.002011369,0.0325832],"study_design_scores_gemma":[0.0001024784,0.00006563003,0.8255759,0.0001361529,0.0006586821,0.00009919199,0.001223147,0.1652168,0.002627051,0.0007938928,0.003395843,0.0001052333],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929548,0.0004311973,0.002997258,0.0003010966,0.0000329955,0.00003177841,0.001967771,0.0001330712,0.00115011],"genre_scores_gemma":[0.9928598,0.00008772097,0.00369255,0.00006754681,0.00001567243,0.00001830189,0.002926044,0.00004342457,0.0002890118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1338465,"threshold_uncertainty_score":0.2692694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01319821167802753,"score_gpt":0.2262805943761461,"score_spread":0.2130823826981186,"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."}}