{"id":"W3000241528","doi":"10.3389/fpls.2019.01677","title":"Change in Autumn Vegetation Phenology and the Climate Controls From 1982 to 2012 on the Qinghai–Tibet Plateau","year":2020,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Biome; Phenology; Vegetation (pathology); Precipitation; Plateau (mathematics); Environmental science; Climate change; Growing season; Physical geography; Climatology; Dormancy; Arid; Normalized Difference Vegetation Index; Atmospheric sciences; Ecology; Ecosystem; Geography; Biology; Agronomy; Meteorology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007810551,0.0001291887,0.0001873712,0.00004720123,0.0001923044,0.00007268121,0.0005251781,0.00005588843,0.00001905938],"category_scores_gemma":[0.0002048349,0.00006708976,0.00001711035,0.0006166676,0.0007754045,0.0002703788,0.0002083876,0.0002502922,0.00008981744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001266898,"about_ca_system_score_gemma":0.00000866051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007630053,"about_ca_topic_score_gemma":0.0007679844,"domain_scores_codex":[0.9984908,0.0001316953,0.0001816392,0.0004382025,0.0003609708,0.0003966803],"domain_scores_gemma":[0.9994606,0.0001813525,0.00007654698,0.0001823437,0.000004038353,0.00009514419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002657405,0.0002177357,0.5326255,0.00003412407,0.00004292353,0.000243224,0.1765566,0.03865966,0.07827814,0.008871529,0.07803718,0.08377601],"study_design_scores_gemma":[0.001375106,0.0001121255,0.7746924,0.00008982162,0.00001014422,0.00000907887,0.001219011,0.2140539,0.0009116195,0.001874708,0.00536433,0.0002877871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971267,0.0001751722,0.0003829295,0.0240936,0.0007155221,0.0007818743,0.00003437309,0.0000251127,0.002524357],"genre_scores_gemma":[0.9918153,0.00006831868,0.001948844,0.006076137,0.0000587007,0.00001001737,0.000005089415,0.000004742498,0.00001280067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2420669,"threshold_uncertainty_score":0.285701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300060137789267,"score_gpt":0.2046596935875259,"score_spread":0.1916590922096333,"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."}}