{"id":"W2917823230","doi":"10.5194/nhess-19-1685-2019","title":"Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds","year":2019,"lang":"en","type":"article","venue":"Natural hazards and earth system sciences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Social Fund; European Regional Development Fund; Comunidad de Madrid","keywords":"Normalized Difference Vegetation Index; Percentile; Environmental science; Generalized extreme value distribution; Moderate-resolution imaging spectroradiometer; Standard deviation; Gamma distribution; Statistics; Estimator; Vegetation (pathology); Remote sensing; Satellite; Mathematics; Geography; Extreme value theory; Physics; Climate change; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006391147,0.0001954698,0.000334926,0.0000909804,0.0003169596,0.0001876664,0.0002686412,0.0000977896,0.00006269515],"category_scores_gemma":[0.00004840583,0.0001201094,0.0000932113,0.00122872,0.0002224435,0.0001749036,0.00006869136,0.000142101,0.00009692938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004756533,"about_ca_system_score_gemma":0.00002278951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002402644,"about_ca_topic_score_gemma":0.0006165464,"domain_scores_codex":[0.9979786,0.00007221875,0.0002422961,0.0006271279,0.0006730093,0.0004067514],"domain_scores_gemma":[0.9993206,0.0001720122,0.00008865297,0.0002171579,0.00003510314,0.0001665014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003386116,0.000010639,0.9714946,0.00004069281,0.00002222219,0.000003147891,0.00008990431,0.009859992,0.001928654,0.0003383223,0.0002582343,0.01591971],"study_design_scores_gemma":[0.0002384089,0.0001474772,0.9732538,0.00004123788,0.00003230574,0.000009120232,0.0002215718,0.02367497,0.0002305071,0.00001422535,0.001922464,0.0002138408],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938698,0.0001934592,0.001796211,0.0004146839,0.0003582079,0.0005245353,0.00005396063,0.00005169407,0.002737479],"genre_scores_gemma":[0.9870204,0.00000348345,0.01222323,0.0002604095,0.00004523097,0.000003439236,0.00001366888,0.000005947992,0.000424173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01570587,"threshold_uncertainty_score":0.4897921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007036987435487499,"score_gpt":0.2335076147700655,"score_spread":0.226470627334578,"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."}}