Grassland Loss Caused by Forest Encroachment in British Columbia from 2015 to 2020
Bibliographic record
Abstract
As a province dominated by forests and mountains, British Columbia (BC) has relatively sparse grasslands accounting for less than one percent of the province area. The BC grasslands have disappeared in large numbers since European settled, and forest encroachment is one of the main reasons for the loss of grasslands. This project aims to study the amount of BC grassland that disappeared due to forest encroachment from 2015 based on the latest data of BC grassland in 2020. Sentinel-2 for southern BC was used for random forest classification to determine the extent of forests and grasslands. The obtained grassland extents were combined with grassland-related data from the BC data catalog to create the most recent grassland map of 2020. Finally, the amount of grassland lost due to forest encroachment in the southern interior of BC was confirmed by comparing with the 2015 grassland extent data. The results show that 1,565.5 hectares of grassland were lost in B.C. compared to 2015, with 829.7 hectares lost to forest encroachment in the southern interior. This study can help the BC government and the Grassland Conservation Council of British Columbia (GCC) update and understand the current range of BC grasslands, and provide the location and quantification of grassland loss, and the BC government can carry out grassland protection actions based on this.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".