A new, proportional method for reconstructing historical tree diameters
Bibliographic record
Abstract
Accurate methods of reconstructing historical tree diameters from increment cores are important because diameter is used in allometric equations to predict stand characteristics and to study stand dynamics. The conventional reconstruction method assumes that the pith is in the centre of the stem. This is often incorrect, as evidenced by a pith increment index quantifying the deviation between the geometric radius of the stem and the chronological radius of a core. I propose a new method which assumes that growth is proportional around the stem and, unlike the conventional method, cannot yield negative historical diameters. These methods were evaluated by calculating the deviations between reconstructed diameters and historical diameter measurements from 164 ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) trees from permanent plots in Arizona and New Mexico. Deviations varied with pith increment index for the conventional method but not for the proportional method, and varied with tree age for both methods at one site. These methods could be used in tandem, with the proportional method applied where the increment from outer ring to pith is measured and the conventional method applied where this increment cannot be measured.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".