{"id":"W3190325350","doi":"10.3390/w13162148","title":"Temporal and Local Heterogeneities of Water Table Depth under Different Agricultural Water Management Conditions","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université Laval; McGill University; Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drainage; Environmental science; Water table; Irrigation; Hydrology (agriculture); Precipitation; Irrigation management; Water use; Groundwater; Geology; Ecology; Meteorology; Geography; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001904849,0.00008182696,0.0001997023,0.0006157201,0.0001510038,0.0002493875,0.0001187247,0.0001825287,0.0005111544],"category_scores_gemma":[0.0004495755,0.00008580667,0.0001310473,0.0006575934,0.0002016049,0.0002490681,0.0002286353,0.0001225356,0.000112452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002492934,"about_ca_system_score_gemma":0.0001194322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008092467,"about_ca_topic_score_gemma":0.01565419,"domain_scores_codex":[0.9998873,0.0000127489,0.000007383876,0.000051524,0.00001946005,0.00002150098],"domain_scores_gemma":[0.9997043,0.00007372072,0.0001029929,0.0000250243,0.00005876554,0.00003523114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004736402,0.00008457101,0.9127127,0.00005379348,0.00009808388,0.0001466659,0.0005928594,0.002205539,0.06689204,0.0001314868,0.0002509293,0.01635779],"study_design_scores_gemma":[0.000001105782,0.00001660509,0.9978289,9.339112e-7,0.000005618519,0.000022975,0.000084893,0.001291539,0.0006238458,0.00002084255,0.00009964403,0.000003070634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988205,0.00004858576,0.0004179626,0.000006965216,0.00000152265,0.000004991587,0.000313543,0.000008155512,0.0003776961],"genre_scores_gemma":[0.9994578,0.00001958967,0.0001286065,0.000002457323,0.000001608408,0.000005162842,0.0002721885,0.000001926698,0.0001106896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008092467,"threshold_uncertainty_score":0.01609069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891397885284438,"score_gpt":0.2147004973733751,"score_spread":0.1957865185205307,"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."}}