Bibliographic record
Abstract
Abstract Top height definitions are often based on the heights of a certain number of the largest trees per unit area, such as the largest 100/ha. Recognizing that results vary with the extent of the reference area, this area is specified in the British Columbia definition, basing top height on the largest tree in a 0.01-ha plot. The problem is how to estimate top height when data is available for larger plots, without the information needed to subdivide them into 0.01-ha subplots. The usual largest 100/ha overestimates the correct value, and we find that the bias can be substantial. We evaluate two alternatives for natural lodgepole pine stands, using data from 0.04- and 0.08-ha sample plots. The improved estimators considerably reduce bias, although some bias due to spatial size autocorrelations remains. Autocorrelation was found to be predominantly positive, and some implications for growth and yield prediction are mentioned. West. J. Appl. For. 20(1):64–68.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".