Caribou Conservation: Restoring Trees on Seismic Lines in Alberta, Canada
Bibliographic record
Abstract
Seismic lines are narrow linear (~3–8 m wide) forest clearings that are used for petroleum exploration in Alberta’s boreal forest. Many seismic lines have experienced poor tree regeneration since initial disturbance, with most failures occurring in treed peatlands that are used by the threatened woodland caribou (Rangifer tarandus caribou). Extensive networks of seismic lines, which often reach densities of 40 km/km2, are thought to have contributed to declines in caribou. The reforestation of seismic lines is therefore a focus of conservation. Methods to reforest seismic lines are expensive (averaging $12,500 per km) with uncertainty of which seismic lines need which treatments, if any, resulting in inefficiencies in restoration actions. Here, we monitored the effectiveness of treatments on seismic lines as compared to untreated seismic lines and adjacent undisturbed reference stands for treed peatlands in northeast Alberta, Canada. Mechanical site preparation (mounding and ripping) increased tree density when compared to untreated lines, despite averaging 3.8-years since treatment (vs. 22 years since disturbance for untreated). Specifically, treated lines had, on average, 12,290 regenerating tree stems/ha, which is 1.6-times more than untreated lines (7680 stems/ha) and 1.5-times more than the adjacent undisturbed forest (8240 stems/ha). Using only mechanical site preparation, treated seismic lines consistently have more regenerating trees across all four ecosites, although the higher amounts of stems that were observed on treated poor fens are not significant when compared to untreated or adjacent undisturbed reference stands.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 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".