Climate Sensitivities of National Parks in the Rocky Mountains
Bibliographic record
Abstract
National Park Service units across the United States have been affected by climate change, and impacts are expected to intensify. Understanding the vulnerability of park resources, assets, and values to climate change is critical for effective and adaptive park management. Climate change vulnerability is a result of exposure, sensitivity, and adaptive capacity. This report provides information on climate sensitivity: the degree to which changes in climate drivers affect a resource, asset, or value, either adversely or beneficially. Here, we present regional-scale information for an area congruent with the Rocky Mountain and Greater Yellowstone Inventory and Monitoring (I&M) Networks, which encompasses areas from southern Colorado to the Montana–Canadian border. We provide regional trends for past, present, and projected future climate drivers, including gradual changes and extreme events. We then summarize the impacts of these climate trends on natural resources, cultural resources, visitor experience, and infrastructure. Throughout the report, we include park-specific examples illustrating sensitivities and their implications for park management. The purpose of this report series is to document current understanding of key climate trends and resource sensitivities to inform park management decisions and provide information that can be readily incorporated into planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".