Allometric exponents as a tool to study the influence of climate on the trade-off between primary and secondary growth in major north-eastern American tree species
Bibliographic record
Abstract
BACKGROUND AND AIMS: Trees invest in both primary (e.g. height) and secondary (e.g. diameter) growth. The trade-off between these investments varies between species and changes with the tree growing environment. To better establish this trade-off, readily available allometric exponents relating height to diameter at breast height (γ(h,dbh)) and stem volume to diameter at breast height (α(v,dbh)) were simultaneously studied. METHODS: Allometric exponents α(v,dbh) and γ(h,dbh) were obtained from 8893 individual tree stem analyses from two broadleaved species (Betula papyrifera, Populus tremuloides) and four conifers (Picea glauca, Picea mariana, Pinus banksiana, Abies balsamea) in the temperate and boreal forests of the province of Quebec, Canada. α(v,dbh) and γ(h,dbh) were related to tree age, stand density index (SDI), and mean temperature (TGS) and total precipitation (PGS) of the growing season. KEY RESULTS: α(v,dbh) and γ(h,dbh) were found to be invariant with PGS and positively related to SDI and TGS for all species except Pinus banksiana. The parameter values associated with SDI and TGS were of higher value for conifers than for broadleaved species. CONCLUSIONS: This suggests that conifers and broadleaved species have different growth patterns. This could be explained by their different mode of development, the conifer species having a stronger apical dominance than broadleaved species. Such results could be further considered in allocation studies to quantify future carbon stocks in managed forests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".