Assessing the impact of oilfield development on native grassland ecosystems with remote sensing
Bibliographic record
Abstract
The native grassland ecosystems in the Canadian prairies, which have been reduced to remnants, continue to be threatened partially as a result of human activities such as agriculture and a significant increase in oilfields from oil exploration and production. The development of oilfields on grasslands often leads to the fragmentation and conversion of native grassland vegetation into oilfields. This is still an increasing trend with newly established oilfields and some of them becoming inactive. The total area occupied by the oilfield is relatively small, but the fragmentation and the effects on vegetation and soil at local and landscape levels for the ecosystem can be significant but are not fully understood. The research focused on understanding the effects of oilfields on native grassland ecosystems with remote sensing techniques, using Monet pasture in the mixed-grass ecoregion of Saskatchewan as the study site. Furthermore, the oilfields were identified and extracted from remotely sensed data using object- and pixel-based image analysis procedures. The spatio-temporal changes of oilfield disturbances in grasslands (over a period of 6 years) were also evaluated, and the local effects of oilfields in grassland were investigated using spectral vegetation indices derived from satellite images to assess vegetation and bare soil changes. The results indicated that a total of 48, 68, and 76 oilfields (distributed within 8 categories: abandoned, active, cased, completed, planned, preset, suspended, and uncategorized) were identified and extracted for the periods of 2016, 2019, and 2022, respectively, in the study area. Out of all the categories, the active oil well was sufficiently extracted. The area covered by the linear and non-linear oilfields increased by 101.6% and 119.1%, respectively (2016–2022). The grassland vegetation cover and water content at varying distances further away from the active oilfields increased and decreased in the study area; variations were observed at different distances (5–50 metres and 10–200 metres) further away from the active oil well and oil road in the years considered. This study will enhance the sustainable management of grasslands. It will also help researchers and grassland managers understand how to effectively identify and monitor oilfield disturbances in grassland ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".