Persistent impact of conventional seismic lines on boreal vegetation structure following wildfire
Bibliographic record
Abstract
Linear disturbances from geological exploration (i.e., seismic lines) have an extensive footprint across much of Canada’s western boreal forest; however, how seismic lines interact with subsequent wildfire remains poorly understood. We assessed whether wildfires effectively mitigate the footprint of seismic lines by promoting forest recovery. We evaluated the forest structure of legacy seismic lines burned in 2001 and 2002 by comparing them against adjacent unburned control plots, using metrics derived from airborne laser scanning (ALS) data collected between 2007 and 2009. ALS metrics identified persistent differences in vegetation height and structure between seismic lines and adjacent forest controls, 7–9 years post-fire. Median canopy height was 2.90 m lower on upland seismic lines and 1.94 m lower on lowland seismic lines than on adjacent controls, corresponding to a 21% and 25% height reduction, respectively. Field surveys revealed greater graminoid and nonvascular plant cover, and lower tall-tree cover and dead vegetative matter, on seismic lines, in comparison with controls. Our results show that tree recovery remains significantly delayed on most upland and lowland burned seismic lines in our study area, and that additional management efforts or longer timescales may be required to restore these fragmented landscapes.
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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 | 0.000 | 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".