Bibliographic record
Abstract
I contributed a paper to volume 60 of the journal. The paper reported on my study of sex-ratio evolution when one sex (females) is helpful but the other sex (males) suffers less from kin competition. I had based my study on a kin-selection model, and so I was dismayed to discover an error in the relatedness calculations therein. Specifically, relatedness coefficients that should have been calculated using a sampling-without-replacement scheme were instead calculated using sampling with replacement. Here, I correct my error and show how it impacts my original findings. I argue that my main conclusions are unchanged. Furthermore, only two new findings contrast with those I presented earlier. First, changing those model details unrelated to the marginal fitness benefits of help does not, in turn, impact substantially the conflict that occurs between mates over the brood sex ratio (I had previously reported some noteworthy impact was possible). Second, help can reduce sex-ratio conflict between mates more effectively when breeders occur in smaller groups (previously, I had said this occurred in larger groups).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".