IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
Bibliographic record
Abstract
Non-invasive measures for investigating physiological responses provide useful tools for understanding how wildlife responds to environmental change. The central and north coasts of British Columbia, Canada, comprise one of the most intact ecosystems in the world; however rapid increases in large-scale human activities, including the threat of oil transport by tankers, could affect ecological processes. I used two biomarkers of physiological responses to investigate how wildlife might be affected by increased or altered patterns in economic activities. I focussed first on the possibility that environmental change could introduce new parasites or alter existing parasite-host dynamics. By examining larval stages of parasites in wolf feces, I found that most parasites reflected seasonal and spatial patterns in wolf trophic interactions. As a complementary approach, I studied micro- and macro-parasite exposure in dogs as sentinels for wild canids and found that they had been exposed to micro-parasites common in canids elsewhere in North America. Results from dogs and wolves reflected positively the health of their environment and provide a baseline for monitoring against future change.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.843 | 0.662 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".