{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003439147,0.0001494107,0.0002281823,0.0006256463,0.0005698044,0.0003810089,0.0001177231,0.0001601132,0.0005794704],"category_scores_gemma":[0.0002965426,0.0001231167,0.0001903355,0.0007232233,0.0003645075,0.0001875022,0.0002475168,0.0001320866,0.0001421071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004658138,"about_ca_system_score_gemma":0.0003879413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02684497,"about_ca_topic_score_gemma":0.0894963,"domain_scores_codex":[0.9998556,0.00004301498,0.00001257234,0.00003759792,0.0000242396,0.00002705495],"domain_scores_gemma":[0.9997603,0.00005656186,0.00008874667,0.00001663634,0.00003559482,0.00004210266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001413246,0.00005637374,0.9646753,0.0000509964,0.0001284163,0.0001242458,0.0004352083,0.0004694363,0.0239931,0.000125018,0.0001233902,0.009677316],"study_design_scores_gemma":[8.617913e-7,0.00001883398,0.9992695,0.000002176982,0.000006410671,0.00003596728,0.0001414525,0.0001094868,0.0002059897,0.00003136066,0.0001764218,0.000001582002],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985043,0.0002757098,0.0002692164,0.00001818583,0.000005556311,0.000002468956,0.0002031584,0.000006163123,0.0007151261],"genre_scores_gemma":[0.9989132,0.0001974883,0.000391282,0.00001743968,0.000007451715,0.000004149791,0.0002785302,0.000004135135,0.0001863039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02684497,"threshold_uncertainty_score":0.05337745,"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."}}