Discriminating between Eggs of Herring Gulls (<i>Larus argentatus</i>) and Great Black-Backed Gulls (<i>Larus marinus</i>) in Eastern Canada
Bibliographic record
Abstract
Distinguishing among eggs of large gull species in mixed colonies is difficult because egg size is variable, size ranges overlap and colors are similar. Regional and yearly differences in egg size of Herring Gulls (Larus argentatus) were compared among three regions (Bay of Fundy, Newfoundland, and low Arctic). In two of these regions (Newfoundland and Bay of Fundy), eggs of Herring Gulls and Great Black-backed Gulls (L. marinus) were measured and discriminant analysis models were created to distinguish between the eggs of these two species. Egg dimensions of Herring Gulls decreased from low Arctic (largest) to Bay of Fundy to Newfoundland (smallest). In both species, where a = first-laid egg, b = second-laid, and c = third-laid, a- and b-eggs were of similar size, but c-eggs were significantly smaller; measurements of a- and b-eggs were pooled. The only annual differences were in a- and b-eggs (treated separately) in Newfoundland; there were no annual differences in c-eggs or in a/b-eggs combined. There were regional differences in a/b-eggs combined, but not in c-eggs. Three separate discriminant function models were created for Newfoundland a/b-eggs, Bay of Fundy a/b-eggs, and Newfoundland/Bay of Fundy c-eggs. Models discriminated 90% or more of the eggs. Length and diameter differ between species and must both be measured to discriminate between Herring and Great Black-backed gull eggs; diameter alone is not reliable. Future application of such models will improve identification of clutches in field situations and lead to more accurate gull population estimates.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".