Bibliographic record
Abstract
Introduction:Increasing the Gray Wolf (Canis lupus) population in Washington state plays two important roles. First, they are an important part of many ecosystems as they are a keystone species. This means that they are a top predator and their absence has reverberating effects on the rest of the ecosystem. As seen at Yellowstone State Park in Wyoming, elk populations ran rampant for many years and reached their peak of roughly 25,000 individuals in 1988 (Kauffman et. al. 2008). The issue lies in the grazing that these elk partake in of a vital habitat producer, Aspen saplings. In 1995 thirty one Canadian Gray Wolves were released into Yellowstone in an attempt to reestablish the population and help curb ungulate populations (Larsen 2006). The park acts as a perfect 'laboratory' to monitor the effects wolves have on elk populations and on vegetation recovery levels. Second, Gray Wolves have been on the Washington Endangered Species list since 1967. This is attributed to the mass hunting and elimination of these animals that were largely perceived as nothing more than pests, mostly by owners of livestock. Washington State, as of December 2011, currently has a Gray Wolf population of twenty seven individuals and only three active breeding pairs (WDFW 2011 & Figure 1). The purpose of this study is to see if Washington has viable habitat for them and if so, how the Gray Wolves can move from each of these habitat islands.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".