Estimated rates of deforestation in two boreal landscapes in central Saskatchewan, Canada
Bibliographic record
Abstract
No national long-term monitoring system exists to estimate temporal changes in the area of forests within Canada. Changes in wooded area, defined as land at least 35% covered by trees or shrubs with a minimum height of 2 m, were estimated for two study areas in central Saskatchewan, Canada. Sequential editions of 1 : 50 000 topographic maps were digitized and analyzed with a geographic information system to quantify changes in wooded area over approximately three decades for the Waskesiu Hills landscape (53°45' N, 106°15' W) and the Red Deer River landscape (52°45' N, 103°00' W). Both study areas were located within the Boreal Plain Ecozone, which was predominantly boreal forest prior to the past century of agricultural land clearing. In the 4570km2 Waskesiu Hills landscape, wooded area decreased by 164 km2 between 1963 and 1990. In the 4692km2 Red Deer River landscape, wooded area decreased by 371 km2 in between 1957 and 1990. Estimated mean annual rates of change in wooded area were 0.19%·year1 and 0.43%·year1 for the former and latter landscapes, respectively. Losses of wooded area were not proportional across three land-use classes. Rates of change for wooded area were small in parks (0.10%·year1 and1.02%·year1) and commercial forests (0.10%·year1 and 0.22%·year1), and larger in predominantly agricultural zones (1.27%·year1 and 1.21%·year1 for the Waskesiu Hills landscape and Red Deer River landscape, respectively). These measured declines in wooded area do not account for losses due to roads, transmission lines, buildings, and other features not represented on topographic maps in an area-proportional manner, but this error is estimated to be very small. The total length of roads increased by 95 km (0.27%·year1 between 1963 and 1990) in the Waskesiu Hills landscape and by 507 km (0.74%·year1 between 1957 and 1990) in the Red Deer River landscape. Expanding infrastructure networks were contrasted by negative rates of change for human population (0.89%·year1 between 1961 and 1991 for the Waskesiu Hills region and 1.19%·year1 between 1956 and 1991 for the Red Deer River region). Within the two study areas, wooded lands that are unprotected by legislation remain vulnerable to future deforestation. Continued clearing of extant forests could jeopardize potential carbon gains from afforestation and reforestation initiatives presently being considered for marginal agricultural lands in western Canada.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".