Anthropogenic activities contribute to changes in forest cover in the shale oil and gas region of Northeastern British Columbia
Bibliographic record
Abstract
The boreal forest ecosystems have been changing due to varying levels of anthropogenic land use processes such as logging, oil and gas activities, and agriculture. However, the cumulative impacts of these processes are likely to lead to a lasting degradation of the boreal forest ecosystem; and thus, contributing to environmental change. In this study, methods from Landscape Ecology, GIS, and remote sensing were used to process Landsat images and spatial data for shale gas infrastructure. These datasets and methods were used for measuring and assessing the forest change pattern in a study area in northeastern British Columbia (BC). The results of the study show that gross loss (5.98%) of coniferous forest cover in the timber harvest land base (THLB) is higher than the rate of gross loss (3.22%) of the coniferous forest cover in the area outside the THLB. However, the rate of net loss in coniferous forest cover is smaller in the THLB than that of outside the THLB (net loss THLB=0.6%; net loss non-THLB=1.7%). These dynamics in forest cover suggest that it is more likely for forest cover to regenerate much faster in the THLB than outside the THLB. The quantity of forest cover loss (0.163%) from shale oil and gas well pads development is more than the amount of forest loss from shale oil and gas access roads (0.017%) and pipeline development (0.057%). A higher amount of forest fragmentation is associated with periods and locations that have a high amount of anthropogenic-induced land classes in the landscape. These results of the study could serve as the information for modelling land change and fragmentation in the future. The finding from this study could assist land managers in the allocation of land uses across space as well as the formulation of effective and efficient policy frameworks and management initiatives.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".