Bibliographic record
Abstract
Grizzly bears in North America face serious conservation challenges, including habitat destruction, poaching, and mortality on roads and railways. In the United States, grizzlies are listed as a threatened species and, in Canada, as a species of special concern. However, the promising convergence of noninvasive genetic sampling (NGS) techniques with hormone and isotope analyses give scientists more tools to identify individuals and populations at risk and to understand the physiological mechanisms at work behind population declines. NGS—the collection of DNA from animals without handling them or piercing their skin—is a study method that has gained traction over the past decade. Most commonly, DNA samples are tufts of hair snared on barbed wire when a bear comes to investigate an irresistible smell (without food reward), such as rotted fish guts or deer blood. DNA from the hair roots is then used to identify individual bears. Gord Stenhouse is a senior grizzly bear biologist with the Foothills Research Institute in Alberta, Canada. He spearheaded the 2008 Alberta grizzly bear census, during which hair samples from bears across the province were analyzed and cataloged. Armed with the identity of each individual, he is now looking at cortisol levels isolated from their hair as an indicator of physiological stress. Stenhouse says that this information could be a useful tool in understanding how bears are responding to climate change and more direct anthropogenic changes to the landscape.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.011 |
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".