Laying–sequence–specific variation in yolk oestrogen levels, and relationship to plasma oestrogen in female zebra finches (<i>Taeniopygia guttata</i>)
Bibliographic record
Abstract
We investigated the relationship between plasma and yolk oestrogens in laying female zebra finches (Taeniopygia guttata) by manipulating plasma oestradiol (E2) levels, via injection of oestradiol-17beta, in a sequence-specific manner to maintain chronically high plasma levels for later-developing eggs (contrasting with the endogenous pattern of decreasing plasma E2 concentrations during laying). We report systematic variation in yolk oestrogen concentrations, in relation to laying sequence, similar to that widely reported for androgenic steroids. In sham-manipulated females, yolk E2 concentrations decreased with laying sequence. However, in E2-treated females plasma E2 levels were higher during the period of rapid yolk development of later-laid eggs, compared with control females. As a consequence, we reversed the laying-sequence-specific pattern of yolk E2: in E2-treated females, yolk E2 concentrations increased with laying-sequence. In general therefore, yolk E2 levels were a direct reflection of plasma E2 levels. However, in control females there was some inter-individual variability in the endogenous pattern of plasma E2 levels through the laying cycle which could generate variation in sequence-specific patterns of yolk hormone levels even if these primarily reflect circulating steroid levels.
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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 | 0.000 |
| 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".