{"id":"W2259611809","doi":"10.5194/bg-13-3819-2016","title":"Spatial and seasonal variations of leaf area index (LAI) in subtropicalsecondary forests related to floristic composition and stand characters","year":2016,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Evergreen; Deciduous; Basal area; Leaf area index; Tropical and subtropical moist broadleaf forests; Spatial heterogeneity; Subtropics; Environmental science; Ecology; Forestry; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001388964,0.00007470719,0.00009803175,0.00004726551,0.00007675112,0.00002443302,0.00007914157,0.00005021742,0.00005749619],"category_scores_gemma":[0.00005348034,0.00004548041,0.00001128302,0.0002462624,0.0003622668,0.0001780395,0.00007495035,0.00003913371,0.00000655728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005229514,"about_ca_system_score_gemma":0.00001255788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005916048,"about_ca_topic_score_gemma":0.001624659,"domain_scores_codex":[0.999211,0.00003603334,0.0001501753,0.0002461798,0.0002130396,0.0001435812],"domain_scores_gemma":[0.9996964,0.00007684373,0.00006107464,0.00007015556,0.000008550898,0.00008702528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001474609,0.00001934319,0.9447394,0.000002701556,0.000001565159,0.000002198543,0.0002380357,0.000007912653,0.04830386,0.00009224485,0.00005683125,0.006521164],"study_design_scores_gemma":[0.0002001954,0.00006669492,0.9970975,0.00004576016,0.000003209638,0.000009370277,0.00002617886,0.001185489,0.0006783943,0.0005075058,0.0001011886,0.00007849253],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997433,0.00001910383,0.001054172,0.001038575,0.0001076121,0.000129398,0.000025594,0.000008898745,0.000183639],"genre_scores_gemma":[0.999499,0.00001704111,0.0003815758,0.00003182162,0.000009809375,9.124081e-7,0.000002744153,0.000002135622,0.00005495549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05235812,"threshold_uncertainty_score":0.1854637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006548080579770875,"score_gpt":0.2049836836023526,"score_spread":0.1984356030225817,"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."}}