National park research fellowships increase capacity and creativity in responding to climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The challenges posed by climate change in national parks and other protected areas demand creative approaches, new ideas, and experiments that are beyond the capacity of any single park or agency staff. Research fellowships provide a critical way that the National Park Service (NPS) and its partners can address the agency’s needs to address climate change adaptation challenges. At least 30 such programs support stewardship-relevant science in national parks. Some national programs and initiatives at Acadia National Park in Maine, Rocky Mountain National Park in Colorado, and Sequoia and Kings Canyon National Parks in California serve as examples of how researchers in these programs are informing restoration, relocation, vegetation and fire management, and resource protection activities; documenting change that has already occurred; providing baseline data on biodiversity; and conducting novel experiments. Successful fellowship programs have strong engagement of resource managers, emphasize communication with management and public audiences, and incorporate ongoing support and evaluation. As a result of these successes, NPS and partners are working to expand and strengthen the sustainability and effectiveness of research grants and fellowships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it