{"id":"W4408644083","doi":"10.1016/j.agrformet.2025.110505","title":"The impact of photovoltaic plants on dryland vegetation phenology revealed by time-series remote sensing images","year":2025,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Photovoltaic Systems and Sustainability","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"National Natural Science Foundation of China","keywords":"Phenology; Remote sensing; Vegetation (pathology); Series (stratigraphy); Environmental science; Vegetation Index; Time series; Photovoltaic system; Meteorology; Normalized Difference Vegetation Index; Geography; Climate change; Ecology; Geology; Mathematics; Statistics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001039405,0.0001334058,0.00009630001,0.0004271623,0.0001135601,0.0003281472,0.00008813736,0.0001802358,0.0008643784],"category_scores_gemma":[0.0003797716,0.00005966242,0.0001118524,0.0004010579,0.00009268148,0.0002322205,0.0001269862,0.0001282074,0.0001152111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002209665,"about_ca_system_score_gemma":0.00009650814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006657197,"about_ca_topic_score_gemma":0.01453912,"domain_scores_codex":[0.9999593,0.000004008451,0.00000225381,0.000009881193,0.00001101688,0.00001353802],"domain_scores_gemma":[0.9998258,0.0000583479,0.00003887038,0.00001076768,0.00003852293,0.00002777146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007848307,0.0001800915,0.8187231,0.0001117959,0.0001573357,0.0007642878,0.0002372719,0.01726098,0.1153044,0.0002937485,0.001494193,0.04468793],"study_design_scores_gemma":[0.000002794724,0.00001974092,0.9933597,0.000002213691,0.00001413377,0.0000421861,0.00007450797,0.005149296,0.001066376,0.00001797888,0.0002484047,0.000002626876],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987417,0.00004985539,0.0001528377,0.0000176243,0.000004893171,0.000001732362,0.0003854349,0.00001135763,0.0006346359],"genre_scores_gemma":[0.9992515,0.00005765458,0.0001064838,0.000006021046,0.000004387788,0.000001130996,0.0003878607,0.000002982684,0.000181933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006657197,"threshold_uncertainty_score":0.01323688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003383225975204539,"score_gpt":0.2157627777775525,"score_spread":0.212379551802348,"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."}}