Regeneration and Growth Following Mountain Pine Beetle Attack: A Synthesis of Knowledge
Bibliographic record
Abstract
The mountain pine beetle (Dendroctonus ponderosae Hopkins; MPB) infestation has altered forests of lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) to an unprecedented extent in British Columbia. After an MPB outbreak, advance regeneration significantly contributed to form a new canopy and stand; however, the time needed to form a new stand depends on site-specific conditions. Assessment of regeneration and the growth of residual trees in stands after MPB attack are critical for three purposes: (1) forecasting long-term development (yield) of attacked stands; (2) selecting stands for growth-improving silvicultural treatments; and (3) forecasting impacts to ecological attributes such as hydrology, habitat, and vegetation types. This article reviews and synthesizes recent research concerning lodgepole pine stand performance after MPB attack in British Columbia. Species composition, abundance, spatial distribution, and overall stand health are described. This information is important for forest managers or practitioners who make decisions regarding management of MPB-attacked stands. Moreover, a number of key gaps exist in our knowledge about factors affecting advance regeneration and the residual trees of MPB-attacked stands. This article presents a list of knowledge gaps for management information and further research initiatives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".