Lakes as the geoindicator of the thermokarst landscapes disturbances after the wildfires, Canadian Arctic
Bibliographic record
Abstract
In situ observations show that the disturbances in the high latitude regions, such as wildfires, are causing permafrost degradation and greatly impact the thermokarst landscapes. In this study we aimed to check if the changes in the lake’s areas can serve as the geoindicator of these disturbances. We selected five wildfire cases, which occurred between 1989 - 2014, located in the lake’s landscapes of the McKenzie River Basin. On the basis of Global Surface Water Dataset (European Commission's Joint Research Centre), we compiled time series of lake surface changes in each fire’s territory. We performed Mann Kendall test, and calculated Sen’s Slope to estimate the trend of the changes. We compared the results with the historical weather data to assess the impact of the weather conditions on the lakes area. We also calculated the intensity of each fire based on Landsat data and the dNBR index. Our Preliminarily results show that the wildfires and changes in the lake’s areas are interrelated; however, fire severity and weather conditions must be taken into account. The changes of the lake’s areas after the wildfire have larger variance compared to the unburned test plots. These results may indicate disturbances in ecosystems and the initiation and acceleration of thermokarst processes. Changes in the lake’s areas are characterised by periodicity, which is disturbed after the fire. The fluctuations in wildfire areas are greater than on the test plots. We observed decreasing trends in the lakes areas short after the fire. This trend begin to decline in the longer term. These results show that the dynamics of changes in the lake’s area is disturbed after the fire. Our method indicates that the lakes in the research territory are sensitive to ecosystem disturbances such as wildfires. In the future this can be used as the indicator of changes in the thermokarst landscapes. Our next step is to supplement the obtained indicator with the use of moisture indexes (e.g., TWI) to determine changes in soil moisture after the wildfire and Sentinel 1 satellite data to study land surface subsidence and soil erosion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".