White spruce regeneration thirty-nine years post-fire in the boreal mixedwoods of Duck Mountain, Manitoba
Bibliographic record
Abstract
The effects of distance to seed source, biotic (vegetation cover) and abiotic (moisture and nutrients) factors on temporal and spatial patterns of white spruce dispersal, establishment and growth were examined at two sites, 39 years post-fire. Partial Mantel tests and partial regressions were used to determine factors affecting recruitment. A growth model, based on empirical results was developed to study growth suppression. A total of 2 042 white spruce trees were aged at the base, 938 of which were also aged at 1.25 m (breast height).The first cohorts to establish (ages 25-39) were affected very little by seed source proximity. Biotic factors such as birch, Corylus cornuta and moss cover were better able to predict abundance of earlier establishing white spruce. The second cohorts to establish (ages 5-24) were most affected by seed source proximity. Abiotic factors such as topographic complexity were also able to predict abundance of later establishing white spruce, in addition to biotic factors. Based on site comparisons it was concluded that Corylus cornuta and other deciduous vegetation limit white spruce recruitment through competition and shedding of broadleaf litter, and may suppress growth, especially of later establishing white spruce. Intraspecific competition between white spruce on these sites often leads to density-dependent mortality, in addition to growth suppression.
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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 | 0.001 | 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".