Comparison of fixed-area plot designs for estimating stand characteristics and western spruce budworm damage in southwestern U.S.A. forests
Bibliographic record
Abstract
Various sampling designs were evaluated using data on stand density, stocking, mortality, and top kill, as influenced by the western spruce budworm (Choristoneura occidentalis Freeman) in 17 stands in New Mexico and Colorado. Efficiency improved as plot size decreased from 0.04 to 0.02 ha for all variables and sampling designs, except for 0.01-ha plots, which required extremely large sample sizes and were subject to bias. Cluster designs were much more efficient than simple random sampling designs, allowing twice the reduction in sample size than was gained by relaxing the allowable error from 10 to 15%. Clusters of two plots were as precise as clusters of three plots. Of the four variables evaluated, density required the largest sample sizes, followed by stocking, percent mortality (for stands where mortality exceeded 10%), and top kill. Few plots were necessary to ascertain that mortality was less than 10%. On average, 10 pairs of 0.02-ha plots would estimate density, stocking, and mortality within a 10% allowable error. A field check of density and stocking variables is recommended, and additional samples are suggested in stands with large percent standard errors associated with those variables.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".