Arrow IFPA Series: Note 4 of 8: Sustainable forest management basecase analysis: The Lemon Landscape Unit pilot project
Bibliographic record
Abstract
This extension note is the fourth in a series of eight that describes a set of tools and processes developed to support sustainable forest management planning and its pilot application in the Arrow Timber Supply Area (TSA). It describes a pilot project designed to evaluate the use of criteria and indicators in developing a sustainable forest management (SFM) basecase and to provide decision support for managers in creating SFM plans. This note outlines how indicators can be used to define management objectives, planning units, and harvesting constraints or “initial thresholds,”and how the resulting SFM basecase was evaluated in trade-off and sensitivity analyses. The process revealed some priority issues in which management objectives for some indicators had significant effects on a measure for the timber criterion (harvest volume) and others had minimal effect. Although the SFM basecase was intended to emphasize non-timber criteria, it nonetheless yielded a greater short- and long-term timber supply than a scenario based on Forest Practice Code rules. This note provides an example of the first iteration of a decision-support process requiring the participation of decision makers and allowing public feedback. Initial results suggest that this decision-support approach has merit and could form an important part of an SFM framework based on criteria and indicators.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".