{"id":"W3134929016","doi":"10.2139/ssrn.3195389","title":"Remote Sensing Applications for Insurance: A Predictive Model for Pasture Yield in the Presence of Systemic Weather","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island; Health Sciences Centre; University of Waterloo; University of Manitoba","funders":"","keywords":"Index (typography); Ground truth; Moderate-resolution imaging spectroradiometer; Spectroradiometer; Predictive modelling; Normalized Difference Vegetation Index; Pasture; Environmental science; Satellite; Computer science; Geography; Leaf area index; Engineering; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000516076,0.0006182607,0.0006177535,0.0002459705,0.000360828,0.0006819636,0.001075138,0.001085674,0.001807958],"category_scores_gemma":[0.001270013,0.0004131144,0.0005184114,0.000354787,0.000350964,0.0006190832,0.0004561198,0.0008288149,0.0002262643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006723593,"about_ca_system_score_gemma":0.0006517156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03794238,"about_ca_topic_score_gemma":0.02125742,"domain_scores_codex":[0.9999185,0.00001652726,0.000004917542,0.00003119665,0.0000156699,0.0000133112],"domain_scores_gemma":[0.9997132,0.000165773,0.00003240632,0.00002290129,0.00004588282,0.00001973759],"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":[0.00004462602,0.00003784852,0.001521678,0.00001179643,0.00001643904,0.000036764,0.00001462807,0.9890988,0.0007010195,0.0007863148,0.0003258985,0.007404196],"study_design_scores_gemma":[0.000001844459,0.000004097492,0.0001976162,5.839157e-7,0.000002271074,0.000002229991,9.623845e-7,0.9994897,0.00007132946,0.0001937703,0.00003411125,0.000001511082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7296824,0.0008322865,0.2611568,0.001076688,0.0001486464,0.00006248509,0.0009515445,0.001362937,0.004726243],"genre_scores_gemma":[0.9901039,0.0001312052,0.007816681,0.00003143779,0.00003184622,0.00002423895,0.0002405598,0.00003232829,0.001587782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03794238,"threshold_uncertainty_score":0.07544309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03037840932315942,"score_gpt":0.263344602666769,"score_spread":0.2329661933436095,"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."}}