UndercoverEisAgenten - The Arctic Permafrost Citizen Science Project
Bibliographic record
Abstract
People in the Arctic have been experiencing severe changes to their environments for several decades. In particular the thawing of permafrost affects the livelihoods of indigenous people and has far-reaching ecological impacts including the additional release of greenhouse gases. \nBy fusing local knowledge on landscape changes in Northwest Canada and remote sensing, we seek to better understand and monitor land surface changes attributable to permafrost thaw. The goal is to investigate permafrost thaw impacts through the acquisition and analysis of imagery from Unmanned Aerial Vehicles (UAVs) and satellites together with young Citizen Scientists from schools in Northwest Canada and Germany. For this, we utilize DJI Mini 2 drones in combination with the Litchi for DJI mobile application as the controller software. This combination allows for the easy creation of flight mission with standardized parameters to enable reproducible results. \nPermafrost landscapes often feature striking polygonal surface structures which change dynamically when thawing processes are in progress. The polygonal landscape structures extend over different spatial scales and can be used to determine the severity of permafrost thaw. While very high-resolution UAV imagery provides detailed insights into the small-scale thermo-hydrological and geomorphological processes, local knowledge and experience is required to identify relevant sites and to set the environmental changes into temporal, cultural, societal, and economic context. These data and background information are urgently needed to improve our prediction on the impacts of permafrost thaw. To this end, school classes in Germany and the Canadian Arctic will collaborate on the analysis of high-resolution remote sensing data. The students will use a mobile application to map striking structures and changes in the land surface on satellites and UAV images. Utilizing feedback from co-creative workshops with German teachers, concepts are being developed to introduce the different topics of this project into school curricula of German high schools. \nThis project will enable the development of better climate adaptation planning tools for local communities and engage Canadian and German students and citizen scientists in Arctic climate research. \nThe project UndercoverEisAgenten, funded by the Federal Ministry of Education and Research in Germany, was initiated in summer 2021.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 teacher head, 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".