Assessing the effect of measurement error in age on dominant height and site index estimates
Bibliographic record
Abstract
Stand age is often hard to measure accurately. Measurement error in age affects height estimates when height is projected from one age to another age. In this study, our objective was to derive an estimator for the variance of the component of projection error associated with the error in age. To evaluate the estimator, a simulation approach was employed and the results showed that the estimator worked well in most cases. When the variance of the error in age increases and the projection is forward, however, the estimator might not work as well as in other cases. Another objective was to examine how the error in age behaved under different height–age models and to what extent it affected height and site index estimates. The effect of the error in age depended on its magnitude, the height–age model, initial value of dominant height and age, and projection length. It was also shown that the effect was sometimes negligible, but it could be considerable in other cases. Data from 80 ponderosa pine ( Pinus ponderosa Dougl. ex Laws.) trees in British Columbia, Canada, were used in an illustrative example.
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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 | 0.038 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".