{"id":"W4385324452","doi":"10.1016/j.fecs.2023.100130","title":"Developing allometric equations to estimate forest biomass for tree species categories based on phylogenetic relationships","year":2023,"lang":"en","type":"article","venue":"Forest Ecosystems","topic":"Forest ecology and management","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Science and Technology Program of Hunan Province; National Natural Science Foundation of China","keywords":"Allometry; Tree allometry; Biomass (ecology); Phylogenetic tree; Ecology; Biology; Mean squared error; Tree (set theory); Statistics; Mathematics; Biomass partitioning","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007591037,0.0002144374,0.0002157335,0.0005428647,0.0006377096,0.00007299749,0.0003415186,0.0001060093,0.000124975],"category_scores_gemma":[0.0006816996,0.0002030417,0.00007960356,0.001926135,0.00007048796,0.0001138378,0.0001511563,0.0000819253,0.00339867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003825703,"about_ca_system_score_gemma":0.00003291125,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001512726,"about_ca_topic_score_gemma":0.0404475,"domain_scores_codex":[0.9982758,0.00007252111,0.0003774032,0.0004488644,0.0002911427,0.0005342395],"domain_scores_gemma":[0.9986485,0.0007027829,0.0001187965,0.00037694,0.00002242341,0.0001306039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002918743,0.00005056091,0.5633736,0.00009359613,0.00002665518,0.000008489891,0.0001486122,0.3028442,0.00009039269,0.1196121,0.01351133,0.0002112075],"study_design_scores_gemma":[0.0004007525,0.0002364545,0.7644134,0.00003234456,0.00002456477,8.869849e-7,0.00008457399,0.1876151,0.0001209918,0.01280772,0.03398295,0.0002802936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6311122,0.00001563427,0.3532287,0.002904796,0.001172292,0.002813701,0.00008301328,0.0004243843,0.00824528],"genre_scores_gemma":[0.9926848,0.000001731533,0.003198942,0.0001113364,0.00007225711,0.001072974,0.000238944,0.00003585656,0.002583196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3615726,"threshold_uncertainty_score":0.9973773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05274713341403122,"score_gpt":0.2746539590707629,"score_spread":0.2219068256567317,"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."}}