Effects of Linear Disturbances and Fire Severity on Velvet Leaf Blueberry Abundance, Vigor, and Berry Production in Recently Burned Jack Pine Forests
Bibliographic record
Abstract
There is limited information on how velvet leaf blueberry (Vaccinium myrtilloides Michx.) responds to fires and existing small forest gaps associated with narrow linear disturbances. We measured the effects of narrow forest linear gaps from seismic lines used for oil and gas exploration versus adjacent (control) forests across a fire severity (% tree mortality) gradient on the presence, abundance (cover), vigor (height), and berry production of Vaccinium myrtilloides in recently (five-year) burned jack pine (Pinus banksiana Lamb.) forests near Fort McMurray, Alberta. Presence was greatest in forests that experienced low to moderately-high fire severities with declines at high fire severity. Abundance did not differ among seismic lines or adjacent forest, nor did it differ along a fire severity gradient. In contrast, vigor and berry production were greater on seismic lines compared to adjacent forests with fire severity positively affecting berry production, but not plant vigor. After controlling for changes in plant cover and vigor, berry production still increased with fire severity and within seismic lines compared with adjacent forests. Our findings suggest that narrow gaps from linear disturbances and fire severity interact to affect the fecundity (berry production) and growth (height) of Vaccinium myrtilloides. This has important implications for assessing the ecological effects of fire on linear disturbances associated with energy exploration in the western boreal forest.
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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".