Research trends in U.S. national parks, the world's “living laboratories”
Bibliographic record
Abstract
Abstract U.S. national parks are essential public assets for preserving natural and cultural resources and for decades have provided natural laboratories for scholarly research. However, park research, and how it may be biased, has not been inventoried at a national scale. Such a synthesis is crucial for assessing research needs and planning for the future. Here, we present the first comprehensive summary of national park research using nearly 7,000 peer‐reviewed research articles published since 1970. We report when and where these studies occurred, what academic disciplines were most represented, and who funded the research. Our findings show that publication rates increased rapidly during the 1990s and 2000s, but since about 2013 have declined. Over half of the studies occurred in five parks, with Yellowstone representing over a third of all studies, followed by Everglades, Great Smoky Mountains, Glacier, and Yosemite. Nearly half of the studies occurred in the Northwestern Forested Mountains ecoregion. The life sciences, particularly ecological studies, contributed the majority of park research, although the earth sciences dominated several arid ecoregions of the West. Federal agencies funded the largest proportion of research, followed by U.S. universities, non‐profit organizations, federal programs (mainly the National Science Foundation), state agencies, and private industry. Over a quarter of the research was supported by international sources. Recent declines in scholarly output suggest that national park research directions and funding opportunities should be examined.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".