{"id":"W1969989309","doi":"10.1155/2013/362895","title":"Temporal and Spatial Variability of Water Surplus in Ontario, Canada","year":2013,"lang":"en","type":"article","venue":"ISRN Soil Science","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canadian Water Network; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Environmental science; Evapotranspiration; Hydrology (agriculture); Precipitation; Drainage; Water table; Spatial variability; Soil water; Water balance; Irrigation; Spatial distribution; Surface water; Groundwater; Soil science; Agronomy; Geography; Geology; Ecology; Environmental engineering; Mathematics; Meteorology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006324928,0.00006865358,0.0001031575,0.00002262355,0.0001142286,0.00001133155,0.0002019472,0.00001947019,0.00113038],"category_scores_gemma":[0.00002437813,0.00004681042,0.000007640953,0.0001053421,0.0009381977,0.0002459516,0.0004137747,0.00007175147,0.00002871152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002473888,"about_ca_system_score_gemma":0.00006425411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9987749,"about_ca_topic_score_gemma":0.9994017,"domain_scores_codex":[0.9990315,0.00002794433,0.0001387348,0.0002660788,0.0002366211,0.0002991455],"domain_scores_gemma":[0.9997505,0.00001936015,0.00002380409,0.0001450016,0.000006790441,0.0000545194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002652006,0.00001883807,0.9971197,0.000002234023,0.000001158958,0.000002813369,0.0005760275,0.0002715605,0.001232448,0.00001098885,0.0002286369,0.0005329329],"study_design_scores_gemma":[0.0001088273,0.00002150382,0.9957064,0.000001799181,0.000001717055,7.830627e-7,0.00004608282,0.0007033404,0.001741756,0.001109091,0.0004908278,0.0000678454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884365,9.302325e-7,0.0000342956,0.0005756516,0.0001102997,0.000121069,4.568836e-7,0.000004260648,0.01071651],"genre_scores_gemma":[0.9992828,8.101039e-7,0.00007299271,0.0001449419,0.00000318255,0.000009395753,5.048296e-7,0.000001433248,0.0004839757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01084623,"threshold_uncertainty_score":0.9997827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005598688930125761,"score_gpt":0.1777765157451108,"score_spread":0.1721778268149851,"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."}}