{"id":"W1565107572","doi":"10.1002/047147844x.aw1506","title":"Water Logging: Topographic and Agricultural Impacts","year":2004,"lang":"en","type":"other","venue":"Water Encyclopedia","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Soil water; Ponding; Water table; Hydrology (agriculture); Environmental science; Soil science; Snowmelt; Surface runoff; Geology; Logging; Surface water; Groundwater; Snow; Geotechnical engineering; Geomorphology; Drainage; Environmental engineering; Forestry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0001271592,0.0003294817,0.0002501988,0.00003219039,0.0001209289,0.0001795737,0.0002137297,0.0002306671,0.005094538],"category_scores_gemma":[0.000002773728,0.00007778503,0.0000900774,0.00006644463,0.00006112414,0.0001540833,0.0001942907,0.0001740726,0.0006226241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001914931,"about_ca_system_score_gemma":0.000002139937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001416837,"about_ca_topic_score_gemma":0.0007918617,"domain_scores_codex":[0.9985743,0.00004466321,0.0002094674,0.0004416151,0.0002191479,0.0005108735],"domain_scores_gemma":[0.9996704,0.00001119588,0.00007376683,0.00008289844,0.00001853055,0.0001431625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006274631,0.0004875337,0.008021169,0.0007031679,0.0006425482,0.0002517026,0.003442605,0.00000711338,0.06845154,0.0013937,0.8712792,0.04525691],"study_design_scores_gemma":[0.000146448,0.00007991745,0.007603806,0.0000559135,0.00005272495,0.00001068087,0.00008817627,2.480406e-7,0.0009573685,0.0005715894,0.9900565,0.0003766021],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1841612,0.0005333016,0.000001072028,0.00495545,0.0007681942,0.0006498118,0.00004561265,0.0003999711,0.8084854],"genre_scores_gemma":[0.2135291,0.001705314,0.0000724019,0.000569478,0.00147421,0.00004490405,0.00103955,0.00002429146,0.7815408],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1187773,"threshold_uncertainty_score":0.9958149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00867619019859011,"score_gpt":0.1985793561333626,"score_spread":0.1899031659347725,"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."}}