The problems with pooling poop: confronting sampling method biases in wolf (<i>Canis</i> <i>lupus</i>) diet studies
Bibliographic record
Abstract
Wolf (Canis lupus L., 1758) diet is commonly estimated via scat analysis. Several researchers have concluded that scat collection method can bias diet estimates, but none of these studies properly accounted for interpack, age class, and temporal variability, all of which could bias diet estimates. We tested whether different scat collection methods yielded different wolf diet estimates after accounting for these other potential biases. We collected scats (n = 2406) monthly from four packs via three scat collection methods (at home sites, at clusters of GPS locations, and opportunistically) in and adjacent to Voyageurs National Park, Minnesota, USA, during April–October 2015. Diet estimates were not affected by scat collection method but did vary temporally, among packs, and by age class. To more accurately estimate wolf population diets, researchers should collect 10–20 adult scats/pack per month from home sites and (or) opportunistically from packs that are representative of the population of interest. Doing so will minimize the potential biases associated with temporal, interpack, and age-class variability.
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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 | 0.221 | 0.338 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".