Field sampling methods on seismic lines: a comparison between circular plots and belt transects
Bibliographic record
Abstract
Seismic lines are linear clearings created for oil and gas exploration. Regeneration on seismic lines in Alberta's boreal forest is limited by slow natural recovery, making them persistent features on the landscape and prompting interest in line restoration through vegetation reestablishment. The goal of this study was to establish an effective woody vegetation sampling method for monitoring seismic line regeneration in boreal mixedwood forests. Data collected in belt transects and circular plots were similar, and doubling the area sampled did not impact the results. Based on ease of implementation, a 30 m × 2 m belt transect in the center of a seismic line is the recommended sampling method. However, if woody understory vegetation is also being measured, dividing the belt transect into smaller areas is recommended as a single belt transect yielded less accurate height data than circular plots. Plots were also measured along seismic lines' edge and inner areas to determine if vegetation recovery differs across the width of the line. While all tree responses did not differ between the plot locations, woody understory vegetation was more abundant when measured along the inner seismic line locations. Therefore, if woody understory recovery is the focus of the sampling program, using plots placed across the seismic line width is recommended to prevent overestimation. These recommendations are intended for measuring woody vegetation recovery on 5 m wide seismic lines in upland mixedwood forests in the boreal forest; implementation in other environments or for other response variables requires additional testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".