The influence of forest harvesting activities on seismic line tree and shrub regeneration in upland mixedwood boreal forests
Bibliographic record
Abstract
Alberta's forests are becoming increasingly disturbed and fragmented by the cumulative effects of anthropogenic disturbances exacerbated by the enduring footprint of seismic lines on the landscape. Forest harvesting and subsequent reforestation activities (e.g., site preparation and tree planting) may facilitate tree growth on seismic lines when forestry companies incorporate them into management areas. In this study, forest regeneration was assessed along transects on and off seismic lines within four cutblocks in northwestern Alberta. Tree, sapling, and shrub characteristics were measured, and tree cookies from a subset of trees on and off seismic lines were used to model height growth. No difference in total tree, sapling, or shrub counts on and off seismic lines was found although trees on seismic lines were growing at slightly slower rates compared to the adjacent cutblock, and Alnus viridis was more abundant on seismic lines. Our results suggest that, despite some species-level differences and modest differences in tree growth rates, trees on seismic lines within cutblocks are regenerating similarly to other trees growing in the post-harvest stand. These findings indicate that forest harvesting, and subsequent reforestation, could represent an important tool in conducting landscape-level restoration in mixedwood forests where seismic lines have not naturally recovered.
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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 | 0.000 |
| 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".