Potential Landscapes for Conservation of the Black‐Tailed Prairie Dog Ecosystem
Bibliographic record
Abstract
ABSTRACT Aim To identify potential landscapes for the conservation of the black‐tailed prairie dog ( Cynomys ludovicianus ; BTPD) ecosystem, across their historical geographical range within the United States. Location Central Grasslands of the United States. Methods We used a structured decision analysis approach to identify landscapes with high conservation potential (HCP) for the BTPD ecosystem. Our analysis incorporated ecological, political and social factors, along with changing climate and land use to maximise long‐term conservation potential. Results The landscapes we identified with HCP (top 30% rangewide) represented 22% of the historical distribution of BTPDs and remained strongholds under projected climate change. We provide a suite of HCP area scenarios to help inform different conservation and management interests, including those that consider projected climate change and jurisdictional (state‐level) boundaries. Main Conclusions Our findings highlight the large conservation potential for BTPDs and associated species, and the maps we generated can be incorporated into other large‐scale, multispecies conservation planning efforts being developed for the Central Grasslands of North America.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".