Forest losses are associated with oil and gas seismic cutlines in Northeastern British Columbia, Canada
Bibliographic record
Abstract
Oil and gas (OG) development involves the creation of geophysical (seismic) cutlines at the time of surveying for OG deposits. The width of the cutlines has reduced over the past two decades due to transition from high to low-impact geophysical surveys for OG deposits. However, the density of the cutlines on landscapes is still an issue of concern because of how long it takes for forests to recover after the creation of the cutlines. Here, using a quartic kernel function, the density of seismic cutlines was calculated for the 69 river basins of northeastern British Columbia. Geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models were used to establish a relationship between the density of seismic cutlines, distance from cutlines, slope, slope aspect, elevation (independent variables), and forest cover loss (dependent variable) between 2000 and 2020. The MGWR analysis shows that seismic cutline density and distance from the cutlines explain 40% of the variance in the forest cover losses that occurred in the river basins [R2 = 0.397, p-value = 0.028]. In the GWR model, seismic cutline density and distance from the cutlines explain 34% of the variance in forest losses [R2 = 0.341, p-value = 0.032]. The study outcome reinforces the robust applicability of MGWR in the study of spatial relations. These models set a tone for further analysis of the spatial relationship between forests and OG activities, and more importantly, provide spatial information for land managers whose role is to manage the impacts of energy development. Graphical Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".