{"id":"W3042460202","doi":"","title":"Monitoring Agricultural Drought in Canada using the Vegetation Drought Response Index (VegDRI)","year":2015,"lang":"en","type":"article","venue":"AGUFM","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Vegetation (pathology); Agriculture; Index (typography); Vegetation Index; Forestry; Geography; Normalized Difference Vegetation Index; Environmental science; Physical geography; Climate change; Ecology; Biology; Computer science; Archaeology","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":[0.0003782776,0.0003098903,0.0002628536,0.001323617,0.001332439,0.000676723,0.000522743,0.0002672473,0.0007616066],"category_scores_gemma":[0.0006992352,0.0001320306,0.0001809206,0.002545547,0.0002419508,0.0002926541,0.0003702389,0.0003900846,0.0001300506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01656559,"about_ca_system_score_gemma":0.01814173,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9960625,"about_ca_topic_score_gemma":0.9980267,"domain_scores_codex":[0.9997713,0.00001336805,0.000009542643,0.00003606525,0.00009869687,0.00007104125],"domain_scores_gemma":[0.9993843,0.00003012316,0.0000532046,0.00001179146,0.0003957136,0.0001247127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002611848,0.000173716,0.896312,0.0001213101,0.0001658137,0.0001521778,0.0006409731,0.007479113,0.005359994,0.0006423358,0.01140465,0.07728677],"study_design_scores_gemma":[0.00002492365,0.00001853752,0.9834396,0.00002032498,0.00002937015,0.00001903004,0.0004981163,0.0100808,0.0007253866,0.00006430765,0.005056207,0.00002333206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981928,0.0005246704,0.0008941134,0.0002996786,0.00001889674,0.00005438879,0.01205765,0.000169489,0.004053123],"genre_scores_gemma":[0.9909511,0.0003739524,0.001935295,0.0000705301,0.00000667494,0.00002812743,0.004729858,0.00001572992,0.001888691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01656559,"threshold_uncertainty_score":0.1201923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01821962028380598,"score_gpt":0.2366317096586588,"score_spread":0.2184120893748528,"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."}}