Accurately ageing trees and examining their height‐growth rates: implications for interpreting forest dynamics
Bibliographic record
Abstract
Summary We examined the validity of classifying tree species as early, mid‐, or late‐successional based on age and height‐growth rates, by comparing the age and height‐growth rates of trees in the boreal forest. Age was first examined using the traditional method of coring 30 cm above the root collar; then dendrochronology was used to locate the root collar and missing annual growth rings. Traditional ageing differentially underestimates tree age; species classified as early successional (Populus tremuloides, Betula papyrifera, and Pinus banksiana) are less severely underestimated than those classified as mid‐ and late‐successional (Picea glauca, Picea mariana, and Abies balsamea) (0–11 vs. 0–43 years), and also have relatively fewer locally missing growth rings. Ageing at the root collar shows that all tree species recruit within 5–10 years after fire and age cannot therefore be used to determine successional status. Mean time taken to grow to each 1‐m increment from the root collar was estimated for each species. Species classified as early successional have relatively higher growth rates between the root collar and the first metre; they are therefore less severely underestimated when aged above the root collar, explaining why they often appear older than species classified as mid‐ and late‐successional. The lack of species differences above 1 m means that height‐growth rates cannot be used to classify these tree species as early, mid‐, or late‐successional. In the boreal forest of Saskatchewan, the rapid recruitment of all tree species after fire, and the short fire cycle mean that the forest dynamics between catastrophic wildfires are driven primarily by the mortality rates of each species.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".