Female-biased sex ratios and the proportion of cryptic male morphs of migrant juvenile Ruffs (Philomachus pugnax) in Finland
Bibliographic record
Abstract
Biases in sex ratio may affect the viability of populations, and may arise for different rea-sons, such as biased primary ratio and differential juvenile or adult mortality of sexes. Global populations of Ruffs are thought to be strongly female biased. To determine the demographic origin of this sex bias, we report the sex ratios among juvenile Ruffs on their southward migration in Finland during 1985–2006. We also quantify the proportion of cryptic, female-like 'faeder'males at this demographic stage, and examine migration tim-ing by sex. We found a strong female bias in juvenile populations; across the study years, 34% of individuals were males. Female juveniles migrated earlier than male juveniles. Faeder males made up ca. 1% of juvenile populations, similar to estimates from mixed-age populations elsewhere. These results, combined with previous studies, confirm the strong female bias and the low frequency of 'faeders'at the juvenile stage of the Ruff. The sex bias might thus demographically originate from sex-allocation strategies by females at the egg stage. Given the genetic determination of the status of faeders, their proportion among juveniles provides the first estimate of the morphs' proportional reproductive suc-cess.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".