An Unusual Case of Common Terns Nesting on a Rooftop in Ontario and an Attempt to Relocate the Colony
Bibliographic record
Abstract
A variety of gull and tern species have adapted to urban environments by nesting on rooftops and other human-made structures. Previous reports of Common Terns (Sterna hirundo) breeding on rooftops have generally involved small numbers of nests (≤25). Here, we report an occurrence of an unusually large number of Common Terns nesting on a rooftop in Port Credit, Ontario, in 2013. A complete survey of the roof was conducted on 31 May, when 1,011 Common Tern nests were counted. A similar number of breeding pairs used this roof in 2014, but the site was largely abandoned by 2015, due to management efforts to displace nesting birds. Common Terns had been observed nesting at this site (≤50 pairs/yr) since 2007; reasons for the sudden and large increase in numbers during 2013 are unclear. We attempted to relocate the colony to a nearby breakwater in 2014 and 2015, by creating artificial nesting habitat and using social attraction techniques. By 26 June 2014, 157 pairs of terns had initiated clutches at the alternate site (breeding success undocumented). The number of tern nests increased to 191 by 17 June 2015; however, all nests were depredated prior to hatching in that year, and the colony was abandoned. When rooftop colonies occur, they represent an opportunity to partner with landowners toward conservation of this declining species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".