Sampling design and precision of basal area growth and stand structure in uneven-aged northern hardwoods
Bibliographic record
Abstract
Sampling design (distribution of plots, plot sizes, and number of sample plots) is an important consideration in planning a field study since it determines the bias and precision of information obtained. In this study, we evaluated the effects of two commonly used sampling designs on the precision of estimates of means related to stand basal area, considered as a whole or by diameter class, and 10-year growth components, in six 2-ha areas located in uneven-aged northern hardwood stands of Quebec managed under selection cutting. The study compares random and systematic sampling designs of one to 10 plots, with plots varying in size from 0.05 ha to 1.25 ha. Increasing the total area sampled (i.e., sampling area) in a stand from 0.05 ha to about 0.25 ha resulted in large precision gains; further increases in sampling area had more limited effects on precision. A sampling area of at least 0.5 ha would be required to obtain a minimally acceptable precision in estimating means of total basal area and 10-year growth components. A much larger sampling area would be required to obtain sufficient precision in estimating mean basal area by diameter class, often required to study stand structure. Precision can also be increased by dividing the sampling area into several smaller plots rather than using a single large plot. We found no clear difference in the results between random and systematic selection approaches.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".