Variable Retention Harvesting: Mortality of Residual Trees and Natural Regeneration of White Spruce
Bibliographic record
Abstract
In this thesis I examined the impacts of variable retention harvesting on residual tree mortality and natural regeneration of white spruce [Picea glauca (Moench (Voss)] in northern Alberta. The VR was done in four overstory canopy compositions (ranging from deciduous dominated to conifer dominated) and at six rates of canopy retention (2%, 10%, 20%, 50%, 75% and 100%). After 10 years there was 32.9 % mortality of aspen (Populus tremuloides Michx.) and 16.9 % mortality of spruce in the VR cuts. Mortality of individual trees was greater with low density of trees, in the conifer stands and for trees with short live crowns, which are large and trees near machine corridors. Natural regeneration of spruce was greatest with higher availability of seed trees (>30 ha-1) and on machine corridors, where stocking reached 74%. By contrast, stocking was ≤14% on retention strips, when seed tree density was ≤11 seed trees ha-1.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".