Comprehensive paternity assignment: genotype, spatial location and social status in song sparrows, Melospiza Melodia
Bibliographic record
Abstract
Comprehensive, accurate paternity assignment is critical to answering numerous questions in evolutionary ecology. Yet, most studies of species with extra-pair paternity (EPP) fail to assign sires to all offspring. Common limitations include incomplete and biased sampling of offspring and males, particularly with respect to male location and social status, potentially biasing estimated patterns of paternity. Studies that achieve comprehensive sampling and paternity assignment are therefore required. Accordingly, we genotyped virtually all males and >99% of 6-day-old offspring over 16 years in a song sparrow (Melospiza melodia) population and used three complementary statistical methodologies to attempt complete paternity assignment for all 2207 offspring. Assignments were highly consistent across maximum likelihood methods that used solely genotype data, and heuristic and integrated Bayesian analyses that included data describing individual locations. Sires were assigned to >99% of all genotyped offspring with ≥95% confidence, revealing an EPP rate of c. 28%. Extra-pair sires primarily occupied territories neighbouring their extra-pair offspring; spatial location was therefore highly informative for paternity assignment. EPP was biased towards paired territorial males, although unpaired territorial and floater males sired c. 13% of extra-pair offspring. Failing to sample and include unpaired males as candidate sires would therefore substantially reduce assignment rates. These analyses demonstrate the integration of genetic and ecological information to achieve comprehensive paternity assignment and direct biological insight, illustrate the potential biases that common forms of incomplete sampling could have on estimated patterns of EPP, and provide an essential basis for understanding the evolutionary causes and consequences of EPP.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".