{"id":"W3024442191","doi":"","title":"Estimating Drought Thresholds for Wheat in the Canadian Prairies Using Remote Sensing Products","year":2013,"lang":"en","type":"article","venue":"AGUFM","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Remote sensing; Environmental science; Agronomy; Geography; Biology","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":[0.000639508,0.0004565359,0.0002381047,0.001376036,0.001164891,0.0009363421,0.0007322371,0.0003516846,0.0006404339],"category_scores_gemma":[0.001726667,0.0002632878,0.0002983194,0.001805022,0.0003774286,0.0005703755,0.0004278492,0.0003956661,0.0001093141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009636209,"about_ca_system_score_gemma":0.007711084,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9871773,"about_ca_topic_score_gemma":0.99354,"domain_scores_codex":[0.9996953,0.00002189669,0.0000146203,0.00006592323,0.00008883912,0.0001135107],"domain_scores_gemma":[0.9994882,0.00008101376,0.0000617495,0.00002680288,0.0002714533,0.00007073244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002786044,0.0001475693,0.8876498,0.00008137844,0.0001963218,0.0001447717,0.0007669348,0.03486909,0.00853552,0.001199972,0.002927747,0.06320222],"study_design_scores_gemma":[0.00002196207,0.00001397208,0.9543514,0.00001539676,0.00003220636,0.00001747459,0.0006181784,0.04290288,0.0006847031,0.0002418656,0.001073891,0.00002599499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944977,0.0001603216,0.001355463,0.00009682948,0.0000039812,0.00003232336,0.001936645,0.00006320308,0.001853433],"genre_scores_gemma":[0.995408,0.0001001147,0.002674364,0.00001496988,0.000001972231,0.00001050834,0.001317146,0.00000881749,0.0004639962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01282275,"threshold_uncertainty_score":0.06991589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116768246186983,"score_gpt":0.2409494973410443,"score_spread":0.2197818148791745,"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."}}