Introducing the young investigator group PermaRisk: "Simulating erosion processes in permafrost landscapes under a warming climate – a risk assessment for ecosystems and infrastructure within the Arctic"
Bibliographic record
Abstract
Permafrost landscapes and high northern infrastructure are under threat of erosion as a consequence of thawing permafrost across the Arctic due to a rapidly warming climate. The young investigator group PermaRisk aims to provide a novel approach for the simulation of erosion and mass wasting processes in permafrost landscapes under a warming climate. The project aims to deliver comprehensive risk assessments for Arctic ecosystems and infrastructure taking into account different processes of permafrost degradation. \nCurrent model approaches used to simulate the degradation of permafrost under a warming climate are highly simplistic since they only consider one-dimensional (top-down) thawing and ignore lateral processes such as soil erosion and mass wasting which are the most abundant form of thaw in many regions. Thus, current model assessments are most likely far too conservative in their estimates of permafrost thaw impacts. It therefore remains uncertain how climate warming and permafrost thaw will affect (i) the intensity of erosion and mass wasting processes and (ii) essential ecosystem functions, landscape characteristics, and infrastructure. It also remains unclear (iii) whether any erosion-induced landscape changes further accelerate permafrost thaw. \nIn order to answer these critical questions, land surface models (LSMs) require a new level of realism in order to adequately project permafrost thaw dynamics. Within the PermaRisk project, the permafrost model CryoGrid3 will be extended with an erosion scheme that allows to represent lateral processes within the limited framework of one dimensional LSMs. The new concept will be applied and validated at three Arctic key sites in Alaska (Deadhorse/Prudhoe Bay), Canada (Arviat), and Siberia (Lena-River-Delta). Furthermore, 21st century climate impact projections for the key sites are scheduled as a basis for thorough risk analyses concerning potential damages to critical ecosystem functions and infrastructure for the three key sites. These risk analyses will focus on near-term impacts for the coming decades as well as on long-term consequences for end of the century.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".