On thin ice: contrasting responses of woody NPP to permafrost thaw
Bibliographic record
Abstract
Boreal forests are located at latitudes that are predicted to experience some of the greatest warming on the planet. Forests growing on permafrost may be particularly vulnerable, with accelerated soil warming and permafrost degradation linked to changes in woody net primary productivity (NPPw). Recent evidence suggests that the responses of NPPw to permafrost thaw are mixed, with both increases and decreases in productivity observed following the onset of permafrost degradation. What determines these contrasting responses is currently poorly understood. This leads to uncertainties in predicting the future vegetation and carbon dynamics in permafrost regions, which propagate to climate projections in Earth System Models. Here, we propose a framework, and a set of hypotheses to explain the observed differences in the response of NPPw to permafrost thaw. We argue that the relationship between permafrost thaw and NPPw is non-linear and determined by a set of climatic and environmental variables. On this basis, we partition ecosystems into classes, and describe their relationships between permafrost thaw and NPPw.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".