Effects of stem and pith eccentricity on the accuracy of basal area increment estimations
Bibliographic record
Abstract
Accurate tree growth quantification is crucial in ecology to assess tree growth. Basal area increment (BAI) is typically calculated from tree rings on increment cores, assuming trees are perfect circles with centered piths. However, trees often have pith offset and stem out-of-roundness, leading to estimation errors. Yet, we do not know how much estimation error results from these eccentricities. Using geometric principles that hold across all tree sizes, we quantified the effects of these eccentricities on BAI accuracy by comparing estimates from four calculation methods and varying core numbers (one to four) against true BAIs taken from cross-section scans. Analysis of 109 cross-sections from 25 temperate species showed that with one core, pith eccentricity accounts for 21% of the error in BAI estimation, and stem eccentricity for 8%. Taking multiple cores, especially two-opposite cores, significantly reduces these errors, with four cores fully accounting for both eccentricities. We recommend using multiple cores to minimize error, with two-opposite cores—taken uphill and downhill—being the most effective approach. We also provide methods for quantifying and reporting pith and stem eccentricity in the field, offering practical guidance for practitioners to calculate estimation errors based on their methods.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".