Large‐Scale Conversion of Forest to Agriculture in the Boreal Plains of Saskatchewan
Bibliographic record
Abstract
Abstract: Despite widespread recognition of the importance of forest loss and fragmentation on biodiversity, the extent and rate of forest loss even in temperate regions remains poorly understood. We documented forest loss and assessed whether road density, rural developments, land quality for agriculture, and land ownership influenced the distribution and rate of change in forest cover for the entire boreal transition zone (49,846 km 2 ) of Saskatchewan, Canada. We used landscape data taken from the Canadian Land Inventory database in forest cover (1996) and Landsat thematic mapper data (1994) to study changes between 1966 and 1994. Forest covered 17,873 km 2 of the study area in 1966 and 13,504 km 2 in 1994. This represents an overall conversion of 73% of the boreal transition zone in Saskatchewan to agriculture since European settlement and an annual deforestation rate of 0.89% over the last 28 years, a rate approximately three times the world average. Mixed‐regressive, spatially autoregressive models explained a considerable portion of the variation in forest cover ( r 2 = 0.83) remaining on the landscape and suggested that less forest remained on lands that (1) were privately owned, (2) had soils with high suitability for agriculture, (3) had high road density, and (4) were in the southern portions of the study area. Strong spatial autocorrelation in the data indicated that areas of remaining forest tended to be spatially clustered. Our ability to predict where deforestation occurred between 1966 and 1994 was poor when we excluded the spatial autocorrelation terms from our model, but it was clear that deforestation was more likely to occur on privately owned lands than on those managed by the provincial government. Despite dramatic changes to forested areas in the boreal transition zone, and despite the importance of this area to a wide variety of forest‐dwelling wildlife, no programs are in place to slow or halt deforestation.
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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 | 0.001 |
| 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".