Current Status and Abundance Trends of Common Terns Breeding at Known Coastal and Inland Nesting Regions in Canada
Bibliographic record
Abstract
The status of Common Terns (Sterna hirundo) breeding in Canada is presented, with abundance trends in regions where data allow. Large (>12,000 pairs) concentrations of Common Terns nested in coastal Newfoundland, long the Gulf of St. Lawrence coast of New Brunswick, and in lakes Winnipeg, Winnipegosis and Manitoba in Manitoba. Nest numbers increased in the four provinces of Atlantic Canada ( 31%; 1973–2010), the Mingan Archipelago National Park Reserve Canada (MANPRC) in the Gulf of St. Lawrence ( 81%; 1986–2009) and possibly in Great Slave Lake ( 10%; 1988–2010). Nest numbers declined (-41%; 1976–2009) in Canadian waters of the North American Great Lakes. Based on recent census data (1999–2010), the number of Common Terns breeding in Canada was estimated at between 82,000–89,500 pairs, with possibly thousands of additional pairs elsewhere in Canada that have never been systematically censused: Manitoba, Saskatchewan, Alberta inland areas of eastern provinces and the boreal forest. Recommendations are that Common Terns be censused in these areas with protocols established for the Great Lakes and annual management be implemented at sites on the Great Lakes earlier identified as “high priority”. Adoption of these recommendations would achieve better understanding of national abundance trends and inform future consevation initiatives.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".