HUNTING TECHNIQUES AND TOOL USE BY NORTH AMERICAN BADGERS PREYING ON RICHARDSON'S GROUND SQUIRRELS
Bibliographic record
Abstract
Techniques used by North American badgers (Taxidea taxus) when hunting Richardson's ground squirrels (Spermophilus richardsonii) were assessed over a 15-year period in southern Alberta to determine the relationship between activity of prey and methods used to capture prey. Badgers frequently hunted hibernating squirrels in autumn, sometimes hunted infants in spring, and rarely hunted active squirrels in summer. Badgers always captured hibernating squirrels and infants underground, usually captured active squirrels underground, and sometimes intercepted fleeing squirrels aboveground. Regardless of season or year, the most common hunting technique used by badgers was excavation of burrow systems, but plugging of openings into ground-squirrel tunnels accounted for 5–23% of hunting actions in 4 consecutive years. Plugging occurred predominantly in mid-June to late July before most ground squirrels hibernated and in late August to late October when juvenile males were active but other squirrels were in hibernation. Badgers usually used soil from around the tunnel opening or soil dragged 30–270 cm from a nearby mound (72% and 22% of 391 plugged tunnels, respectively) to plug tunnels. The least common (6%), but most novel, form of plugging used by 1 badger involved movement of 37 objects from distances of 20–105 cm to plug openings into 23 ground-squirrel tunnels on 14 nights. Aimed movement of objects to plug openings into burrow systems occupied by ground squirrels qualified this badger as a tool user.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".