{"id":"W4390038634","doi":"10.1139/cjfr-2023-0171","title":"Species-specific and generalized allometric equations for improving aboveground biomass estimations of 33 understory woody species in northeastern China forest ecosystems","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Understory; Tree allometry; Allometry; Biomass (ecology); Ecology; Ecosystem; Environmental science; Forest ecology; Woody plant; Forestry; Biology; Geography; Canopy; Biomass partitioning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001725229,0.0006770462,0.0003693603,0.0009076358,0.0002471829,0.0004145583,0.0005211167,0.0002096133,0.0004762203],"category_scores_gemma":[0.002787343,0.0002723747,0.0006193624,0.0008638251,0.0002012794,0.0006452907,0.0004913131,0.0003102613,0.00009187125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006884558,"about_ca_system_score_gemma":0.0008853252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02079966,"about_ca_topic_score_gemma":0.04982477,"domain_scores_codex":[0.9995797,0.0001655948,0.00004717358,0.0001015312,0.00007939318,0.00002662333],"domain_scores_gemma":[0.9993937,0.0002241006,0.0001203501,0.00005687469,0.000178737,0.00002630394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008284549,0.0001179939,0.5855039,0.0002068097,0.0003119774,0.0001477522,0.0006407867,0.129889,0.01727177,0.002464717,0.0010505,0.2623118],"study_design_scores_gemma":[0.00001822163,0.00008389835,0.2918947,0.00002167708,0.0001214373,0.00009280336,0.0001727686,0.7023616,0.002430687,0.001518655,0.001233431,0.00005006404],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.917972,0.0002022436,0.0807004,0.00003664549,0.00001181421,0.00005829266,0.0003483583,0.0001878492,0.000482426],"genre_scores_gemma":[0.90922,0.0001502383,0.08917219,0.00001553152,0.000005265124,0.00009011485,0.0006979386,0.00002973169,0.0006189762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02079966,"threshold_uncertainty_score":0.04135722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07596717879983583,"score_gpt":0.2934583147012781,"score_spread":0.2174911359014423,"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."}}