Impacts of thermokarst on permafrost carbon losses and ecosystem services
Bibliographic record
Abstract
Permafrost thaw is altering northern ecosystems and the services they provide at scales ranging from local subsidence to global climate feedbacks. In organic-rich peatlands, thermokarst initiation and spread rates are increasing with rising mean annual air temperatures, changes in wildfire, and human land use. This presentation will outline empirical and modeling approaches to better understand the consequences of thermokarst in peatlands as well as other types of northern terrains on carbon cycling, wildlife, and other aspects of ecosystem services. We are using fine scale datasets and remote sensing to map thermokarst coverage and expansion in both the Northwest Territories, Canada and interior Alaska. Using chronosequences and regional gradients, we are studying thermokarst impacts along gradients of time-since-thaw. Through a Permafrost Carbon Network synthesis, we developed conceptual and numerical models to understand how thermokarst development (formation, stabilization, re-accumulation of permafrost in some conditions) affects carbon storage and release. We are using a combination of empirical and modelled data to test hypotheses about climatic, ecological, and Quaternary controls on thermokarst rates and subsequent impacts on ecosystem services. We demonstrate that thermokarst in peat-rich landscapes are hotspots for permafrost carbon release primarily through methane emissions, have the potential to impact hunter movement and safety, and affect caribou habitat quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".