Simulating the response of natural ecosystems and their fire regimes to climatic variability in Alaska
Bibliographic record
Abstract
The dynamic global vegetation model MC1 was used to examine climate, fire, and ecosystems interactions in Alaska under historical (19221996) and future (19972100) climate conditions. Projections show that by the end of the 21st century, 75%90% of the area simulated as tundra in 1922 is replaced by boreal and temperate forest. From 1922 to 1996, simulation results show a loss of about 9 g C·m 2 ·year 1 from fire emissions and 360 000 ha burned each year. During the same period 61% of the C gained (1.7 Pg C) is lost to fires (1 Pg C). Under future climate change scenarios, fire emissions increase to 1112 g C·m 2 ·year 1 and the area burned increases to 411 000 481 000 ha·year 1 . The carbon gain between 2025 and 2099 is projected at 0.5 Pg C under the warmer CGCM1 climate change scenario and 3.2 Pg C under HADCM2SUL. The loss to fires under CGCM1 is thus greater than the carbon gained in those 75 years, while under HADCM2SUL it represents only about 40% of the carbon gained. Despite increases in fire losses, the model simulates an increase in carbon gains during the 21st century until its last decade, when, under both climate change scenarios, Alaska becomes a net carbon source.
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 | 0.016 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".