A method to improve confidence in paternity assignment in an open mating system
Bibliographic record
Abstract
Molecular techniques have allowed researchers studying mating systems to determine the identity of extra-pair sires, providing more accurate measures of individual realized reproductive success. Yet, an existing problem in such studies is the inability to assign paternity to individuals that have not been captured. This frequently arises when only a proportion of the population is sampled or when visitors from outside the study area have access to the breeding population. It is therefore difficult to assign paternity in situations where not all candidate sires are sampled because some assignments may be incorrect, especially when using a likelihood-based approach. This study outlines a method that combines two different programs, GERUD 1.0 and CERVUS 2.0, to increase confidence in paternity assignment. The benefit of using these programs in conjunction is that GERUD 1.0 can reconstruct genotypes of males that are not sampled in families where the female was sampled, and CERVUS 2.0 can use this information to better assign paternity because more information is provided. We show how applying this method to Least Flycatchers (Empidonax minimus), a sub-oscine bird with an open mating system, substantially increases confidence in paternity assignments.
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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".