Potentiell paverkan fran Alaskas skogsbrander pa S:t Eliasbergens glaciarer, Yukon Kanada
Bibliographic record
Abstract
How great potential effect does the Black carbon emitted from the boreal forest fire region of Alaska have on the retreating glaciers of the St. Elias Mountains? In this study climate and forest fire history data of Alaska was run in the HYSPLIT wind trajectory model to generate trajectories originated from large occurring fires in Alaska from 2005 to 2014. Results show a small percentage of trajectories passing the St. Elias Mountains and an expected pattern of a correlation between passing trajectories and density of amount forest fires. Interdisciplinary climate research is indicating an increase in global temperatures with consequences such as an upswing of forest fires in the Northern Hemisphere. Inner Alaska is fire prone due to a combination of prevailing droughts during the summer season and frequent lightning ignition as a result from homogeneous vegetation and topography. Downwind from Alaska’s forest fire region is the ice field of the St. Elias Mountains, these glaciers are one of the fastest retreating due to increasing global temperatures and possible deposition of soot from Alaskan forest fires. Forest fire emits black carbon, which when deposited on snow or ice surfaces will decrease the albedo and accelerate the melting rate. Previous studies on ice cores from the St. Elias have investigated traces of combustion products from biomass burning. This indicates a possible record of historic forest fires in ice cores. The small percentage of passing trajectories in this study suggests that most large forest fires in Alaska might not be registered in the St. Elias ice cores.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".