Effects of oil sands disturbances on shrub and tree structure along forest edges in Alberta's boreal forest
Bibliographic record
Abstract
Deforestation causes forest fragmentation and associated edge effects. The boreal forest of Alberta, Canada has undergone substantial fragmentation via the creation of seismic lines, roads, and wellpads for resource exploration and extraction, but their associated edge effects have not been fully assessed, particularly for the latter two footprint types. We examined how these disturbances influence forest composition and structure along anthropogenic forest edges in the oil sands region of northeastern Alberta. We then used generalized linear models to test distance to edge responses in tree and shrub density given treatment (disturbance) type and forest canopy composition. Our results indicate the presence of edge effects, even along narrow seismic lines. Tree and shrub density and tree basal area were greater at the forest edge, being two times greater at 1 m from the forest edge relative to intermediate interior forest distances (∼30 m). Variations in tree basal area, tree density, and shrub and sapling density were best explained by interactions between disturbance type, distance from the forest edge, and % conifer composition. This study demonstrates that anthropogenic disturbances from energy exploration in the boreal forests cause changes in tree and shrub density (structure), and this effect is most pronounced in deciduous-dominated forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".