REGIONAL ECOLOGY, ECOSYSTEM GEOGRAPHY, AND TRANSBOUNDARY PROTECTED AREAS IN THE ST. ELIAS MOUNTAINS
Bibliographic record
Abstract
This study characterizes the broad‐scale ecology of the St. Elias region of Yukon, Alaska, and British Columbia and assesses the implications for ecosystem‐based management of the region's protected areas, including Kluane, Wrangell‐St. Elias, and Glacier Bay National Parks, and Tatshenshini‐Alsek Provincial Park. An interdisciplinary, map‐based process was used to synthesize information, and the fields of regional ecology and ecosystem geography provided the foundation for analysis. Results illustrate that the protected areas share several regional‐scale ecosystem components with each other and with surrounding areas, including globally significant populations of large mammals and other wildlife species as well as vegetation communities that experience a full suite of natural disturbances with little human intervention. The valleys of the Tatshenshini, Alsek, and Copper Rivers serve as important links between coastal and interior areas as well as conduits for the movement of biota. However, connectivity is not distributed equally across the region, and the four core national parks have linkages with adjacent areas that are as strong, and in many cases stronger, than among themselves. The management challenge is not a matter of linking separate protected areas to create networks. Instead, it lies in integrating existing protected areas with each other and with surrounding areas and resisting small changes that have incremental and cumulative impacts. Interagency cooperation is seen as a key component in facilitating this, and some success has been achieved on the scale of single issues and specific resources. The challenge ahead is to build on this success by strengthening existing institutional frameworks and working toward more comprehensive efforts. Similar lessons can be derived for other complex mountain landscapes and northern regions where large protected areas and multiple land management agencies exist.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".