The changing effects of Alaska’s boreal forests on the climate systemThis article is one of a selection of papers from The Dynamics of Change in Alaska’s Boreal Forests: Resilience and Vulnerability in Response to Climate Warming.
Bibliographic record
Abstract
In the boreal forests of Alaska, recent changes in climate have influenced the exchange of trace gases, water, and energy between these forests and the atmosphere. These changes in the structure and function of boreal forests can then feed back to impact regional and global climates. In this manuscript, we examine the type and magnitude of the climate feedbacks from boreal forests in Alaska. Research generally suggests that the net effect of a warming climate is a positive regional feedback to warming. Currently, the primary positive climate feedbacks are likely related to decreases in surface albedo due to decreases in snow cover. Fewer negative feedbacks have been identified, and they may not be large enough to counterbalance the large positive feedbacks. These positive feedbacks are most pronounced at the regional scale and reduce the resilience of the boreal vegetation – climate system by amplifying the rate of regional warming. Given the recent warming in this region, the large variety of associated mechanisms that can alter terrestrial ecosystems and influence the climate system, and a reduction in the boreal forest resilience, there is a strong need to continue to quantify and evaluate the feedback pathways.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".