{"id":"W4231888170","doi":"10.5194/hessd-12-6467-2015","title":"Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anomaly (physics); Watershed; Spatial ecology; Anomaly detection; Stability (learning theory); Temporal scales; Environmental science; Soil science; Mathematics; Computer science; Data mining; Physics; Ecology","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":[0.0005001511,0.0003593188,0.0003203917,0.001470272,0.0002445483,0.0006386032,0.0005437933,0.000287517,0.0003984299],"category_scores_gemma":[0.001250093,0.00014198,0.0006162249,0.001710209,0.0003267696,0.0005480327,0.0004989103,0.0003964805,0.0000983373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000927689,"about_ca_system_score_gemma":0.001112119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1426906,"about_ca_topic_score_gemma":0.1715879,"domain_scores_codex":[0.9998091,0.00002630677,0.00001088117,0.00008370894,0.00004018801,0.0000299205],"domain_scores_gemma":[0.9995738,0.0001190453,0.000080739,0.0000512656,0.0001416412,0.0000334834],"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":[0.0002523274,0.0001669904,0.3305416,0.00009396284,0.0004631643,0.0003538538,0.0003163433,0.4899739,0.04033135,0.002617295,0.001435579,0.1334537],"study_design_scores_gemma":[0.000004582338,0.000008409485,0.05411768,0.000003345371,0.00001682944,0.00001878604,0.00005024338,0.944061,0.0008844985,0.000553754,0.0002702517,0.00001058538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.903286,0.0001214262,0.09448139,0.00009033633,0.000009077015,0.0000261039,0.001026648,0.0003542331,0.0006048363],"genre_scores_gemma":[0.9802393,0.00004103897,0.01857863,0.000009752398,0.00000616983,0.0000152357,0.0008912293,0.00002075058,0.0001977343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1426906,"threshold_uncertainty_score":0.2837202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02586423436414033,"score_gpt":0.233247515570224,"score_spread":0.2073832812060837,"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."}}