{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000269371,0.000324676,0.000272514,0.0003977612,0.0001981848,0.0003832184,0.0005279829,0.0002993206,0.005417229],"category_scores_gemma":[0.0004151212,0.0001463478,0.0001437462,0.0006181052,0.0001258741,0.0008517117,0.0003577716,0.0002570072,0.00138314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000224114,"about_ca_system_score_gemma":0.000517005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005180785,"about_ca_topic_score_gemma":0.01010765,"domain_scores_codex":[0.9998896,0.00001222869,0.000005488865,0.00002386725,0.0000578654,0.00001102655],"domain_scores_gemma":[0.9997326,0.00003812388,0.00002043397,0.00006950217,0.0001013632,0.00003805283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009967347,0.0004603945,0.0494789,0.0004200323,0.000127924,0.0007086474,0.0002627593,0.01874226,0.3398016,0.003112771,0.04774281,0.5381452],"study_design_scores_gemma":[0.000421668,0.001073114,0.1415051,0.00008959269,0.0001914353,0.001907005,0.0002861361,0.2351727,0.356773,0.00245473,0.2600001,0.0001254071],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4772051,0.0007745643,0.419963,0.0006582349,0.0002079644,0.001492427,0.02351285,0.04299541,0.03319045],"genre_scores_gemma":[0.794543,0.0005357651,0.1757341,0.0002374287,0.00004858718,0.0005139054,0.01551449,0.0004250316,0.01244778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005417229,"threshold_uncertainty_score":0.01812243,"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."}}