Carbon and biomass partitioning in balsam fir (Abies balsamea)
Bibliographic record
Abstract
Balsam fir (Abies balsamea (L.) Mill) was extensively sampled to investigate the effects of forest management practices, site location, within-crown position, tree component (i.e., stem, foliage, branches and roots), and tree social classes on biomass and carbon (C) partitioning at the individual tree level and across ecological regions. The sites were located in three ecologically distinct forest regions of west-central New Brunswick, Canada. There were no significant differences in %C content of trees across ecological regions or across tree social classes. However, at the individual tree level, significant differences were evident in biomass and C allocation between different parts of the tree, between treatment types (i.e., unmanaged and pre-commercially thinned stands) and between within-crown positions, indicating the need for separate estimates of biomass and C content of tree components to obtain more precise estimates of quantities at the stand level. Calculating stand C content based on constant allocation values, as is commonly done, produced errors of up to 15% compared with the values calculated in this study. Three allometric equations of biomass and C that account for partitioning among different parts of the tree were developed and compared: (1) a third-order polynomial, (2) a modified inverse polynomial and (3) a modified Weibull equation. Diameter at breast height (DBH) was used as the only explanatory variable to describe fresh biomass, dry biomass and C content. All regressions derived showed a high correlation with DBH, with most r2 values > 0.95. A comparison of the equation results showed that the modified Weibull equation gave consistent results with the best overall fit and was the simplest of the three equations investigated. The regressions can be used to estimate forest biomass and tree C content at the stand level, given specific information on DBH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".