Resource allocation in fledglings of the rhinoceros auklet under different feeding conditions: an experiment manipulating meal size and frequency
Bibliographic record
Abstract
By manipulating meal size and frequency in an alcid, the rhinoceros auklet (Cerorhinca monocerata (Pallas, 1811)), we examined two hypotheses: (1) poorly fed chicks allocate resources preferentially to developing organs essential for fledging, and (2) intermittently fed chicks deposit more lipids than regularly fed ones. Chicks were fed normal (NORMAL; 40–80 g, mean meal mass in a normal year), small (LOW; 26–54 g, half of NORMAL), or large (HIGH; 80–160 g, twice as much as NORMAL) amounts of sandlance (Ammodytes personatus Girard, 1856) every day or the large meal (80–160 g) every 2 days (INTERMITTENT). Chicks fed more food grew faster. The HIGH group had the greatest fledging mass and shortest fledging period. The wingspan and brain mass of fledglings did not differ among groups. The heart, liver, and breast muscle at fledging were 15%–25% smaller in the LOW group than in the NORMAL group but did not differ between the NORMAL and HIGH groups. The total lipid was 43% greater in the HIGH group than in the NORMAL group, and that of the LOW group was 38% smaller. The INTERMITTENT group had a similar lipid mass to the NORMAL group. Chicks feeding on small meals seemed to maintain the growth of organs essential for fledging, while chicks feeding on large meals seemed to deposit a surplus as lipid rather than allocate more to the development of organs.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".