A data compilation and synthesis of the impacts of seismic surveys on surface soil properties in boreal Alberta, Canada
Bibliographic record
Abstract
Linear clearings of vegetation to perform geophysical surveys, called seismic lines, are created for oil and gas exploration in boreal Canada and often persist on the landscape for decades after disturbance. Therefore, an assessment of environmental conditions on seismic lines is needed to inform restoration efforts. This study aimed to compile surface soil properties (upper 5–15 cm; dry bulk density, organic matter content, organic matter bulk density, volumetric water content, and water content by mass) on and off seismic lines across upland, transitional, and peatland ecosystems in northern Alberta, Canada ( N = 1638). Soil properties differ between seismic line and reference samples, especially on older “conventional” lines. Changes included higher dry bulk density, lower organic matter content, and elimination of microtopographic variability. Changes in dry bulk density can, in part, be explained by a reduction in organic matter content, but altered carbon cycling and/or compaction are also important. Restoration techniques such as inverted mounding create an entirely distinct soil condition, with higher mean bulk densities and lower organic matter contents than both on and off seismic lines. Therefore, an assessment of microtopographic recovery should be conducted before prescribing restoration treatments to limit further degradation of soil structure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".