{"id":"W3046586734","doi":"10.1088/1748-9326/abaad9","title":"Alignment of tree phenology and climate seasonality influences the runoff response to forest cover loss","year":2020,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Environmental science; Streamflow; Surface runoff; Baseflow; Snowmelt; Precipitation; Climate change; Hydrology (agriculture); Drainage basin; Deforestation (computer science); Ecology; Geography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001435509,0.0001477651,0.000169583,0.00002713687,0.0003650493,0.00001926978,0.000416611,0.00004230599,0.0006291328],"category_scores_gemma":[0.0001161957,0.0001062996,0.00004241033,0.0001589908,0.002217179,0.0001331447,0.001709731,0.0002203321,0.000797493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001265857,"about_ca_system_score_gemma":0.000002961098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006679767,"about_ca_topic_score_gemma":0.00002831849,"domain_scores_codex":[0.997522,0.0006084852,0.0002064885,0.0004408244,0.0006598002,0.0005624034],"domain_scores_gemma":[0.999163,0.0003606048,0.00004672184,0.0002555146,0.00000137449,0.0001727916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001236261,0.00007078268,0.9047983,0.00001406847,0.00005814154,0.00004544793,0.002650636,0.002261296,0.08124918,0.00004949933,0.006410519,0.001155901],"study_design_scores_gemma":[0.0004179075,0.0003654997,0.9702863,0.000005708333,0.00001325198,0.000002228682,0.0005281647,0.0001470031,0.002561855,0.0001060052,0.02543882,0.000127303],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9155145,0.00005622845,0.00004634275,0.0834988,0.00002231968,0.0004258747,0.00002261658,0.00001074586,0.0004025392],"genre_scores_gemma":[0.9904683,0.0001367085,0.0001016245,0.009109615,0.00002405289,0.00006084767,0.000003521828,0.00001059088,0.00008472712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07868733,"threshold_uncertainty_score":0.9999805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753508986540581,"score_gpt":0.2823340023865938,"score_spread":0.254798912521188,"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."}}