Estimating optimum sampling size to determine weighted core specific gravity of planted loblolly pine
Bibliographic record
Abstract
Data from a variability study of loblolly pine ( Pinus taeda L.) based on weighted core specific gravity (WCSG) were examined to show how costs and variance estimates are used in designing efficient sampling strategies. Increment cores for the determination of WCSG were taken from 3957 trees across six distinct physiographic regions in the southeastern United States. More variability was found to exist among stands than within stands. This indicates that reducing the variation of the mean of WCSG can be accomplished by sampling more stands and fewer trees in the region of interest. The number of stands and trees to sample is dictated by the maximum allowable cost and the precision required of the sample mean, and formulas are given for such calculations. The estimate of among-stand variability was found to be similar among the regions of interest, whereas larger within-stand variation was found to exist in the South Atlantic and Hilly regions. The standard error of the mean was found to increase with an increase in the age at which the stand was sampled. When sampling across multiple stands (at any age), little if any gain in the precision of the standard error of the mean is gained by sampling more than 15 trees. In the general case where one is interested only in the value of WCSG in one stand and precision or cost–time factors are not of consideration, it would suffice to sample between 45 and 55 trees at any age.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".