Science and the St Elias: an evolving framework for sustainability in North America's highest mountains
Bibliographic record
Abstract
The past, present, and future contributions of science in the St Elias Mountains, and its relationship with regional development, resource management, and traditional ecological knowledge is examined. Science has evolved from an early foundation of exploration, through stages of resource inventories and surveys, to deductive scientific research and, more recently, a promising reconnection with traditional knowledge. Directly and indirectly, events such as the Klondike Gold Rush, construction of the Alaska Highway, creation of the Arctic Institute of North America's Kluane Lake Research Station, and establishment of protected areas have helped foster scientific activities in the region. In turn, this scientific perspective has influenced regional development by providing detailed information that has been utilized, to varying degrees, in resource use, planning, and decisionmaking. Over the past decade, management of the region has become less sectoral and more cooperative in nature, due partly to the implementation of co‐management agreements, regional land use planning, and settlement of first nations’ land claims. Incorporating both science and traditional knowledge into this process through collaborative endeavours such as long‐term ecological monitoring, adaptive management, and information integration will contribute to ecosystem‐based management of the St Elias and ensure that both perspectives play an integral role in sustainable development of the region.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.053 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".