The Importance of Alaska for Climate Stabilization, Resilience, and Biodiversity Conservation
Bibliographic record
Abstract
Alaska is globally significant for its large tracts of intact habitats, which support complete wildlife assemblages and many of the world’s healthiest wild fisheries, while also storing significant amounts of carbon. Alaska has 1/3 of United States federal lands, the bulk of the United States’ intact and wild lands, and over half of the country’s total terrestrial ecosystem carbon on federal lands. Managing Alaska’s public lands for climate and biodiversity conservation purposes over the next 30–50 years would provide meaningful and irreplaceable climate benefits for the United States and globe. Doing so via a co-management approach with Alaska’s 229 federally recognized tribes is likely not only to be more effective but also more socially just. This paper lays out the scientific case for managing Alaska’s public lands for climate stabilization and resilience and addresses three primary questions: Why is Alaska globally meaningful for biodiversity and climate stabilization? Why should Alaska be considered as a key element of a climate stabilization and biodiversity conservation strategy for the United States? What do we need to know to better understand the role of Alaska given future scenarios? We summarize evidence for the role Alaska’s lands play in climate stabilization, as well as what is known about the role of land management in influencing carbon storage and sequestration. Finally, we summarize priority research that is needed to improve understanding of how policy and management prescriptions are likely to influence the role Alaska plays in global climate stabilization and adaptation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".