{"id":"W4252660999","doi":"10.4095/220059","title":"A Soil Moisture Sensorweb for Use in Flood Forecasting Applications","year":2003,"lang":"en","type":"report","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Flood myth; Environmental science; Flood forecasting; Moisture; Water content; Hydrology (agriculture); Meteorology; Geology; Geography; Geotechnical engineering; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006111377,0.0002761898,0.0003267409,0.0000970325,0.0001002465,0.00006870623,0.0003730951,0.0005158792,0.0000677586],"category_scores_gemma":[0.000533748,0.0002484513,0.0001086853,0.0002817723,0.0001248438,0.0001301638,0.0002911198,0.0003669939,0.00006524153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008445525,"about_ca_system_score_gemma":0.00006144943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004474803,"about_ca_topic_score_gemma":0.003429215,"domain_scores_codex":[0.9980182,0.00002978754,0.0004410568,0.0006277993,0.0004669416,0.0004162165],"domain_scores_gemma":[0.9988693,0.0001665016,0.0001915256,0.000695598,0.00003022064,0.00004684733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002802219,0.0008014253,0.5737008,0.001091298,0.0001688801,0.00007098993,0.0004220468,0.005130964,0.002308872,0.0005402754,0.3268739,0.08886257],"study_design_scores_gemma":[0.0003595873,0.00005438817,0.008181846,0.0001124549,0.00005918848,0.00006923568,0.0001955704,0.0003870844,0.00598065,0.003945131,0.9798027,0.0008521467],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4686685,0.0006443812,0.07638056,0.002013987,0.003573093,0.01891961,0.0009033575,0.004730292,0.4241662],"genre_scores_gemma":[0.2927293,0.000574237,0.5108867,0.0002133755,0.0008952765,0.008300168,0.0004065932,0.0003815363,0.1856128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6529288,"threshold_uncertainty_score":0.9999968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1244882423511759,"score_gpt":0.3054274658256642,"score_spread":0.1809392234744883,"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."}}