Vegetation Changes in Alberta Oil Sands, Canada, Based on Remotely Sensed Data from 1995 to 2020
Bibliographic record
Abstract
There are rich oil and gas resources in Alberta oil sand mining area in Canada. In the 1960s, the Canadian government decided to increase the mining intensity. However, the exploitation will bring many adverse effects. In recent years, more people pay attention to the environmental protection and ecological restoration of mining areas, such as issues related to changes in vegetated lands. Thus, the authors used the Landsat-5 TM and Landsat-8 OLI remote sensing images as the basic data sources, and obtained the land cover classification maps from 1995 to 2020 by ENVI. Based on the NDVI, NDMI and RVI, three images in each period are processed and output to explore the long-term impact of exploitation. The results show that from 1995 to 2020, the proportion of vegetation around mining areas decreased sharply, the scale of construction land in the mining area increased, and the vegetated land was changed to land types such as tailings ponds, oil sand mines and other land types. In addition, three vegetation indexes decreased from 1995 to 2020. Although the exploitation of oil sand mining areas brings great economic benefits, the environmental protection (especially vegetation) in oil sand mining areas should be paid more attention.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".