Exclusion of Ring-billed Gulls (Larus delawarensis) from recreational beaches using canid harassment
Bibliographic record
Abstract
Abstract Ring-billed Gull (Larus delawarensis) populations have dramatically increased throughout their geographic range with the largest concentrations in the Great Lakes region of Canada and the United States. Large populations of gulls cause conflict with humans at recreational beaches, where their effects on human health and safety include bacteria contamination from gull feces. We used border collies to harass and exclude gulls from beaches in summer 2012 and 2013, then measured gull numbers and Escherichia coli. Dogs were effective at reducing gull numbers by 56–76% during continuous and noncontinuous dog treatment periods. Levels of E. coli were lower on dog-treated beaches, but only during the first half of the summer. Mixed modeling analysis showed presence of dogs was a strong predictor of gull numbers and E. coli levels, with variation among dogs, possibly related to age. Noncontinuous use of dogs, applied within an integrated beach management framework, can provide a nonlethal method for reducing gull use and E. coli levels at recreational beaches.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".