What Does Climate Change Mean for the Arctic? How is Alaska Being Affected?
Bibliographic record
Abstract
The Environmental and Energy Study Institute (EESI) held a Congressional briefing on March 15, 2005 on the Arctic Climate Impact Assessment (ACIA) 1 and climate change impacts already observed in Alaska. The assessment, released in November 2004, is an intergovernmental report based on a four-year scientific study of the Arctic conducted by an international team of 300 scientists and sponsored by the eight arctic nations (Canada, Denmark, Finland, Iceland, Norway, Russia, Sweden and the United States) and six indigenous people's organizations. It concludes that the average winter temperatures in Alaska and other arctic regions have increased by 4 to 7 degrees Fahrenheit (3-4 degrees Celsius) in the past 50 years, twice the rate of the rest of the globe, and are projected to continue rising for the next century. Alaska is being affected by climate change and experienced its warmest summer on record in 2004, characterized by its worst fire season, unprecedented insect outbreaks, and significant coastal erosion. The warming has caused a decline in summer sea ice extent and thickness, allowing seasonal storms to increasingly erode portions of the Alaskan coastline. The Government Accountability Office (GAO) estimates costs of $100-400 million to move a single endangered Alaskan village, with some 184 villages seen as susceptible to flooding and erosion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".