Integrating multi-discipline teams into forest planning: A case study in west-central Alberta
Bibliographic record
Abstract
Millar Western Forest Products Ltd. completed its 2007–2016 detailed forest management plan for its forest management agreement area by engaging multi-discipline expertise to better manage forest values. Disciplines were formed into impact assessment groups, which were charged with developing indicators and targets for identified values and objectives and with constructing models to predict the impacts of forest management activities. Three different modeling approaches were used in developing the plan and the best results were obtained by integrating indicators directly into the forecasting model even when the indicators were simple versions of complex models. Integrating indicators into the forecasting model reduced the penalty cost on other indicators, permitted the model to find better solutions, provided almost immediate feedback to the plan development team, permitted more scenarios to be investigated, and provided a better understanding of the dynamics and tradeoffs. The other two techniques used in the DFMP involved the assessment of forecasted scenarios with more complex discipline specific models, which provided more detailed information about the indicators. Key words: forecasting, forest management planning, Patchworks, forest management modeling, timber supply analysis, trade-off analysis, VOIT
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".