Incorporating multi-cohort old aspen and mixedwood dynamics into a long-term forest management plan
Bibliographic record
Abstract
The 2006–2026 Sustainable Forest Management Plan for Manitoba’s Duck Mountain Provincial Forest (DMPF) was required to model the Future Forest Condition 200 years from the present. This was a significant challenge for forest stands that were already old at the beginning of the planning and modelling horizon. In the DMPF, the majority of stands were currently old and near the end of the sampled yield curve. We incorporated aspen and aspen–mixedwood Permanent Sample Plot remeasurement data, aged up to 200 years old, into the yield curves. This extended the sampled volume–age data of the yield curves from 120 to 200 years old. The recent discovery of multi-cohort aspen and mixedwood stands was also incorporated into modelling the Future Forest Condition of the Sustainable Forest Management Plan. Multi-cohort stand dynamics replaced previous modelling assumptions of single-cohort, even-aged stands “breaking up” and suffering catastrophic loss (i.e., death age) where volume equals zero at stand ages 140 years or older. Modelling the multi-cohort stand dynamics resulted in a significantly different Future Forest Condition due to: maintenance of a continuous forest canopy over the entire landscape; higher modelled biodiversity in older stands due to multiple canopies, abundant snags, and coarse woody debris; and the avoidance of a large age class imbalance due to stands being available for harvest longer, but at a lower volume.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".