{"id":"W3197153784","doi":"","title":"GIS Spatial-Temporal Modeling of Water Systems in Greater Toronto Area, Canada","year":2018,"lang":"en","type":"article","venue":"Journal of Earth Science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Aquifer; Hydrology (agriculture); Geology; Drainage basin; Drainage; Digital elevation model; Elevation (ballistics); Drainage system (geomorphology); Spatial distribution; Spatial variability; Groundwater; Surface water; Groundwater recharge; Environmental science; Remote sensing; Geography; Cartography; Geotechnical engineering","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":[0.0003181748,0.0003751003,0.0004148837,0.001337966,0.001452269,0.001909711,0.001505339,0.0005719472,0.005091329],"category_scores_gemma":[0.00163678,0.0004059244,0.000660092,0.003861553,0.0005652059,0.0005556472,0.0005768157,0.0005076736,0.0003524346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03794904,"about_ca_system_score_gemma":0.02681772,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9979168,"about_ca_topic_score_gemma":0.997889,"domain_scores_codex":[0.9997471,0.00003791493,0.00002101455,0.00005285759,0.00006303251,0.00007809767],"domain_scores_gemma":[0.9993618,0.0001237463,0.00005195806,0.0000264185,0.0003500358,0.00008611323],"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":[0.0001469817,0.00009683133,0.1490404,0.000176336,0.0001743095,0.0002849227,0.0008225127,0.8103597,0.0006335217,0.009467506,0.01424337,0.01455354],"study_design_scores_gemma":[0.00004915762,0.0000188139,0.08988608,0.00006357465,0.00006363192,0.00003969971,0.001775509,0.895405,0.0003403141,0.00109009,0.01121187,0.00005619804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568155,0.0006335859,0.003479262,0.001229118,0.00004414952,0.00009518824,0.02653971,0.0003195223,0.01084391],"genre_scores_gemma":[0.9883928,0.0003807573,0.00236779,0.00003464466,0.000006658966,0.00004141957,0.004203269,0.00003204019,0.004540617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03794904,"threshold_uncertainty_score":0.2753409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02698199057204837,"score_gpt":0.262054799654441,"score_spread":0.2350728090823926,"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."}}