{"id":"W4412042192","doi":"10.5194/essd-17-3219-2025","title":"China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022","year":2025,"lang":"en","type":"article","venue":"Earth system science data","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"China; Environmental science; Resolution (logic); Climatology; Geology; Geography; Computer science; Archaeology","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":[0.0006379353,0.001011669,0.0005090333,0.002798741,0.0003372983,0.0004903876,0.0008853164,0.0003985569,0.003134572],"category_scores_gemma":[0.0009161295,0.0002986151,0.000693864,0.004224986,0.0001879615,0.0004905489,0.0005191749,0.0003977371,0.002683586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009961539,"about_ca_system_score_gemma":0.001665169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1312067,"about_ca_topic_score_gemma":0.1531395,"domain_scores_codex":[0.9995828,0.00004025996,0.00005575615,0.0001230015,0.0001337135,0.00006439416],"domain_scores_gemma":[0.9990014,0.00004798918,0.0001191552,0.0001979598,0.0005391636,0.00009423366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006552309,0.0003285003,0.2939366,0.001757132,0.0006383421,0.0007538423,0.0003093646,0.03662539,0.006123215,0.001734674,0.5783219,0.078816],"study_design_scores_gemma":[0.0001755421,0.0000890121,0.6997397,0.0001567959,0.0001296353,0.0001925698,0.0002247757,0.03605633,0.003412386,0.0005412975,0.2591743,0.0001077465],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04361098,0.0002461474,0.001165333,0.00009053323,0.00005283799,0.00008648187,0.9517419,0.0008913444,0.002114528],"genre_scores_gemma":[0.04018502,0.0001187596,0.001898918,0.00002479812,0.00001318895,0.0001148017,0.9568287,0.00003015225,0.0007857416],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1312067,"threshold_uncertainty_score":0.260886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348761642537488,"score_gpt":0.2608264286053386,"score_spread":0.2473388121799637,"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."}}