{"id":"W4213266228","doi":"10.1139/as-2020-0025","title":"Natural variation in snow depth and snow melt timing in the High Arctic have implications for soil and plant nutrient status and vegetation composition","year":2022,"lang":"en","type":"article","venue":"Arctic Science","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snowmelt; Snow; Environmental science; Nutrient; Normalized Difference Vegetation Index; Vegetation (pathology); Arctic; Arctic vegetation; Physical geography; Ecology; Agronomy; Biology; Geology; Climate change; Tundra; Geography; Geomorphology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006795545,0.00006190428,0.00006851935,0.0001348825,0.0005126121,0.0001146355,0.00008944576,0.00001256412,0.0000174247],"category_scores_gemma":[0.00007769725,0.00004912056,0.000006810094,0.0003070456,0.0001364426,0.000290807,0.0000247848,0.00009587376,4.511957e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002532808,"about_ca_system_score_gemma":0.00003523355,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008122288,"about_ca_topic_score_gemma":0.02968055,"domain_scores_codex":[0.9992033,0.0000583151,0.0001205525,0.000240398,0.0001648611,0.000212565],"domain_scores_gemma":[0.9992335,0.000564774,0.00005317205,0.00007864928,0.00002547357,0.00004447118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003898519,0.00002289366,0.9803708,0.00003522009,0.000001545737,0.000001502078,0.01041708,0.0007130087,0.001578958,0.001212574,0.000005254452,0.005602163],"study_design_scores_gemma":[0.0003174459,0.00008066381,0.9462778,0.00002219107,0.00000652111,0.00004140278,0.001164602,0.04770466,0.00003108449,0.004263802,0.00002387093,0.00006595199],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972423,0.0004206508,0.00005391159,0.001649298,0.0001034691,0.0003133369,0.0001798539,0.000003999493,0.00003312891],"genre_scores_gemma":[0.9988425,0.0001718868,0.0001817389,0.0003637575,0.00001724743,0.00002317357,0.0003964896,0.000001299718,0.000001928548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04699165,"threshold_uncertainty_score":0.9984827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03615562299226948,"score_gpt":0.2625282466396711,"score_spread":0.2263726236474016,"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."}}