Contrasting changes in surface waters and barrens over the past 60 years for a subarctic forest–tundra site in northern Manitoba based on remote sensing imagery
Bibliographic record
Abstract
Intensified warming in the Arctic and Subarctic is resulting in a wide range of changes in the extent, productivity, and composition of aquatic and terrestrial ecosystems. Analysis of remote sensing imagery has documented regional changes in the number and area of ponds and lakes as well as expanding cover of shrubs and small trees in uplands. To better understand long-term changes across the edaphic gradient, we compared the number and area of water bodies and dry barrens (>100 m2) between 1956 (aerial photographs) and 2008–2011 (high-resolution satellite images) for eight ∼25 km2 sites near Nejanilini Lake, Manitoba (59.559°N, 97.715°W). In the modern landscape, the number of water bodies and barrens were similar (1162 versus 1297, respectively), but water bodies were larger (mean 3.1 × 104 versus 681 m2, respectively) and represented 17% of surface area compared with 0.4% for barrens. Over the past 60 years, total surface area of water did not change significantly (16.7%–17.1%) despite a ∼30% decrease in numbers of small (<1000 m2) water bodies. However, the number and area of barrens decreased (55% and 67%, respectively) across all size classes. These changes are consistent with Arctic greening in response to increasing temperature and precipitation. Loss of small water bodies suggests that wet tundra areas may be drying, which, if true, may have important implications for carbon balance. Our observations may be the result of changes in winter conditions in combination with low permafrost ice content in the region, in part explaining regional variations in responses to climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".