An approach to optimizing field data collection in an inventory by compartments
Bibliographic record
Abstract
This study presents models for the expected error of the total volume and saw timber volume due to sampling errors of stand measurements. The measurements considered are horizontal point sample plots, stem numbers from circular plots, sample tree heights, sample order statistics (i.e., quantile trees), and sample tree heights from the previous inventory. Different measurement strategies were constructed by systematically varying the numbers of these measurements. A model system developed for this study was used in a data set of 170 stands to predict the total volume and saw timber volume of each stand with each measurement strategy. The errors of these volumes were modeled using stand characteristics and the numbers of measurements as predictors. The most important factors affecting the error in the total volume were the number of horizontal point sample plots and height sample trees. In addition, the number of quantile trees had a strong effect on the error of saw timber volume. The errors were slightly reduced when an old height measurement was used. There were significant interactions between stand characteristics and measurement strategies. Thus, the optimal measurement strategy varies between stands. A demonstration is provided of how constrained optimization can be used to find the optimal strategy for any one stand.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".