The Importance of Natural Variability in Lake Areas on the Detection of Permafrost Degradation: A Case Study in the Yukon Flats, Alaska
Bibliographic record
Abstract
ABSTRACT Long‐term lake area change has previously been measured to detect the temporal rate and spatial extent of permafrost degradation. However, the natural intra‐ and interannual variability of lake areas has not been considered explicitly and quantitatively, which can substantially interfere with the detection of long‐term lake area change associated with permafrost degradation. In order to better understand the natural background variability of lake areas, we used Landsat 7 images obtained on 11 dates from 1999 to 2002 to quantify the intra‐ and interannual lake area variability for a 4224 km2 study area within the Yukon Flats, Alaska. Total lake areas ranged from 179 km2 (22 August 1999) to 326 km2 (6 June 2000). Even within a single year (year 2000), the total lake area decreased by 42 per cent from 6 June to 16 August, well exceeding the previously reported trends for long‐term decrease (14% and 18%) for the Yukon Flats. Both intra‐ and interannual area variability in August and September were smaller than in June and July, suggesting that images from later in summer are more reliable for detecting long‐term change in lake area. Variability of no‐closure lakes was twice that of closed‐basin lakes. Intra‐annual area changes in closed‐basin lakes can be explained by the intra‐annual water balance, defined as cumulative precipitation minus evaporation between two consecutive dates within the same year. For a given period, the total lake area was correlated more strongly with the water balance since the preceding October than with the water balance in the preceding 12 months. Spatial heterogeneity in the intra‐annual area change of individual lakes was observed, which might be caused by different topographical, geological and permafrost characteristics around and beneath the lakes. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 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) | 0.000 | 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".