Identification of representative sampling heights for specific gravity and moisture content in plantation-grown loblolly pine (<i>Pinus taeda</i>)
Bibliographic record
Abstract
Conventionally, increment cores collected at breast height (1.4 m) have been used to measure wood properties of standing loblolly pine (Pinus taeda L.) trees. This height has been used because of the ease of sampling and cost involved in extracting the cores. In this study, the efficacy of a breast-height core to represent whole-tree specific gravity (SG) and moisture content (MC) was examined. The sampling height that best represents whole-tree SG and MC was identified using the correlation between volume-weighted whole-tree SG and MC with SG and MC measurements collected at different heights within a tree. We found a high correlation between observed whole-disk SG and MC with volume-weighted whole-tree SG and MC at most sampling heights. The strength of the correlation followed a skewed parabolic curve form for both wood properties. The strongest correlations were observed between 4.6 and 6.1 m, with marginally lower correlations at the base of the tree, and the weakest correlations were observed towards the tip of the tree. In addition, it was found that the number of stands and trees that need to be sampled to achieve a certain accuracy in overall mean whole-tree SG and MC was greater if breast height was used as a sampling height compared with the most representative sampling heights identified (4.6 m for SG and 6.1 m for MC).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".