{"id":"W2810916354","doi":"10.1002/ecs2.2309","title":"Quantifying snow controls on vegetation greenness","year":2018,"lang":"en","type":"article","venue":"Ecosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Energistyrelsen; Canada Excellence Research Chairs, Government of Canada; National Aeronautics and Space Administration; National Science Foundation; Miljøstyrelsen; Aarhus Universitet","keywords":"Normalized Difference Vegetation Index; Snow; Snowmelt; Vegetation (pathology); Environmental science; Precipitation; Physical geography; Arctic; Greening; Climatology; Atmospheric sciences; Elevation (ballistics); Climate change; Geography; Ecology; Geology; Meteorology; Oceanography; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0004895553,0.0003185913,0.0002879282,0.0005117867,0.0003290194,0.0006508661,0.0002003473,0.0001327695,0.000547147],"category_scores_gemma":[0.0006870512,0.0001394209,0.0002946617,0.0004604516,0.0002759902,0.0003325459,0.0003861591,0.0001313936,0.0000651451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006794339,"about_ca_system_score_gemma":0.00033736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02922092,"about_ca_topic_score_gemma":0.04351698,"domain_scores_codex":[0.9998254,0.00005271905,0.000009793238,0.00005520811,0.00002445123,0.00003249172],"domain_scores_gemma":[0.9995329,0.0001755875,0.0001301957,0.0000407541,0.00007113257,0.00004937967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002127137,0.00005546028,0.9127633,0.00007446574,0.000402924,0.0001060776,0.0001904384,0.03030328,0.04604697,0.0003918879,0.0002053026,0.009247145],"study_design_scores_gemma":[0.000003163041,0.00002756248,0.97283,0.00000813473,0.00003581278,0.00002361059,0.0001225197,0.02449014,0.001793881,0.0003539071,0.0003044444,0.000006799816],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987971,0.00009008012,0.0005649934,0.0000089558,0.000001752798,0.000002560741,0.0002171974,0.00001028021,0.0003071055],"genre_scores_gemma":[0.9995942,0.00002619121,0.0001877944,0.00000440364,0.000001472122,0.000001601123,0.0001346019,0.000002946328,0.00004687416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02922092,"threshold_uncertainty_score":0.05810165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04356136199163092,"score_gpt":0.2528290095962733,"score_spread":0.2092676476046424,"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."}}