Evaluating Height‐Age Determination Methods for Jack Pine and Black Spruce Plantations Using Stem Analysis Data
Bibliographic record
Abstract
Abstract Six height‐age determination methods (Graves, Lenhart, Carmean, Newberry, ratio, and ISSA) were evaluated for their accuracy and sensitivity to sample size in determining height‐age pairs using stem analysis data from plantation-grown black spruce (Picea mariana[Mill.] B.S.P.) and jack pine (Pinus banksiana Lamb.) trees from Ontario, Canada. Twenty-three disks (sections) were used from 102 jack pine and 93 black spruce trees each for evaluation. The Graves, ratio, and Newberry methods were unbiased for determining height‐age pairs forboth black spruce and jack pine across the site productivity gradient and different crown classes. However, on the basis of the magnitude of height prediction bias, reconstructed tree profiles, and the amount of information required for height‐age determination, the Graves method withat least 13 stem sections is recommended for height‐age determination.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".