Linking ecologically based productivity information to timber supply analysis units using site series sampling
Bibliographic record
Abstract
Timber supply analyses are used to estimate the possible harvest level of timber volume over the long term. Site index is one of the inputs of these analyses. When site index is underestimated, as is often the case for older stands, it will lead to underestimated yields. This creates a significant negative effect on harvest levels in the timber supply analyses. Better site index information is obtainable by using ecologically based site indices; however, an efficient way of applying the site index estimates is needed. The purpose of this project was to develop a technique for incorporating better site index estimates into timber supply analyses. We used simple random sampling to determine the proportion of each site series in a management unit. Site index estimates were available for these site series. To link the site index information to timber supply analysis units, we initially created analysis units using inventory information. The site series proportions were then used to form new ecologically based analysis units, and yield tables were generated from the associated site index information. After an area was harvested in the timber supply model from an inventory-based analysis unit, it was allocated to an ecologically based analysis unit in proportion to the area that the site series occupied in the timber harvesting land base. Once an area was placed into a new analysis unit, it remained there for the duration of the timber supply analysis. We tested this method in the Bulkley Timber Supply Area, where it resulted in a 26% increase in the long-term sustainable harvest level.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".