UndercoverEisAgenten - Monitoring Permafrost Thaw in the Arctic using Local Knowledge and UAVs
Bibliographic record
Abstract
The Arctic is experiencing severe changes to its landscapes due to the thawing of permafrost influenced by the twofold increase of temperature across the Arctic due to global warming compared to the global average. This process, which affects the livelihoods of indigenous people, is also associated with the further release of greenhouse gases and also connected to ecological impacts on the arctic flora and fauna. These small-scale changes and disturbances to the land surface caused by permafrost thaw have been inadequately documented.To better understand and monitor land surface changes, the project "UndercoverEisAgenten" is using a combination of local knowledge, satellite remote sensing, and data from unmanned aerial vehicles (UAVs) to study permafrost thaw impacts in Northwest Canada. The high-resolution UAV data will serve as a baseline for further analysis of optical and radar remote sensing time series data. The project aims to achieve two main goals: 1) to demonstrate the value of using unmanned aerial vehicle (UAV) data in remote regions of the global north, and 2) to involve young citizen scientists from schools in Canada and Germany in the process. By involving students in the project, the project aims to not only expand the use of remote sensing in these regions, but also provides educational opportunities for the participating students. By using UAVs and satellite imagery, the project aims to develop a comprehensive archive of observable surface features that indicate the degree of permafrost degradation. This will be accomplished through the use of automatic image enhancement techniques, as well as classical image processing approaches and machine learning-based classification methods. The data is being prepared to be shared and analyzed through a web-based crowd mapping application. The project aims to involve the students in independently acquiring data and developing their own scientific questions through the use of this application.In September 2022, a first expedition was conducted in the Northwest Territories, Canada and UAV data was collected with the assistance of students from Moose Kerr School in Aklavik. The data consists of approximately 30,000 individual photos taken over an area of around 13 km². The expedition also provided an opportunity for the students to learn about the basics of data collection and the goals of the collaborative permafrost survey, which included the incorporation of local knowledge to address the questions of the local community.By involving school students in the data acquisition, classification and evaluation process, the project also seeks to transfer knowledge and raise awareness about global warming, permafrost, and related regional and global challenges. Additionally, a connection through the shared research experience between students in Germany and Canada is established to enable the exchange of knowledge. The resulting scientific data will provide new insights into biophysical processes in Arctic regions and contribute to a better understanding of the state and change of permafrost in the Arctic. This project is funded by the German Federal Ministry of Education and Research and was initiated in 2021.
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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 | 0.000 | 0.000 |
| 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".