Nutrient allocation for egg production in six Atlantic seabirds
Bibliographic record
Abstract
How species allocate nutrients to egg production is an important question in contaminant analyses. Seabird eggs are sampled frequently in such studies, so it is important to know the source of nutrients in these eggs if the source of the contaminants is to be traced. We used a stable-isotope approach to evaluate the relative importance of locally derived nutrients (income breeding) and stored nutrient reserves (capital breeding) in six species of Atlantic seabirds (Arctic Tern, Sterna paradisaea Pontoppidan, 1763; Common Tern, Sterna hirundo L., 1758; Atlantic Puffin, Fratercula arctica (L., 1758); Common Murre, Uria aalge (Pontoppidan, 1763); Razorbill, Alca torda L., 1758; Leach’s Storm-Petrel, Oceanodroma leucorhoa (Vieillot, 1818)) breeding in the Bay of Fundy. We found that all species either were income breeders or adopted an intermediate strategy whereby varying proportions of locally derived nutrients were incorporated into eggs. Each species’ migratory behaviour is likely a main factor in determining the amount of endogenous nutrients used in egg formation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".