Errors in estimating tree age: implications for studies of stand dynamics
Bibliographic record
Abstract
Errors in estimates of tree ages from increment cores can influence age-class distributions, affecting inferences about forest dynamics. We compare methods of height correction of increment cores taken above ground level by examining how resulting errors affect age-class distributions of ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) and Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco). We compared the sapling (corrections based on the average basal age of breast high saplings) and the ground methods (corrections based on the average difference in age between ground and coring height) with a regression model we developed to overcome traditional assumptions of temporal and spatial homogeneity in early growth. Where early growth differed among mature trees or between modern saplings and mature trees, the regression method estimated age better than the two other methods. All methods of height correction over- or under-estimated tree age by at least 10 years and up to 30 years, indicating that age cannot be related to independent events of periodicities less than 1020 years, such as El Niño, without accounting for error. Monte Carlo simulations demonstrated that error from height corrections affected the shape of age-class distributions by generating spurious regeneration pulses. We suggest that the magnitude of this error should govern the width of analytical age-classes to scale interpretations within the confidence of age estimates.
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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 | 0.040 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".