Bibliographic record
Abstract
He started working at Yellowstone in the spring of 1994 worked in many other areas and transferred to that area to help with the education of the wolf reintroduction proposal. When Yellowstone became the first national park in 1872, there was a program to kill the wolfs native to the area. The last original wolf in Yellowstone was killed in 1926. It took the park a long time to realize the mistake it was to kill a native animal. The plan to reintroduce wolves to the park took many years. The final plan involved catching wild wolves in Canada, bring them to the park, allow each pack to become acclimated to the area, and then release them. In January 1995, 14 wolves (3 packs) were brought from Alberta, Canada. In January 1996, 17 wolves (4 packs) were brought from British Columbia, Canada. The Native American tribes perform ceremonies to wish the wolves good luck and a long life in the park. The wolf population now is about what it was before the park rangers began hunting them. He hopes 100 years from now the Yellowstone management does not give up when confronted with a crisis. He hopes that they know that there is always a way to correct or resolve any situation; just like the management before them.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.158 | 0.069 |
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".