The snail's love-dart delivers mucus to increase paternity
Bibliographic record
Abstract
Many of the seemingly bizarre animal behaviours can be understood only by acknowledging the power of sex to shape evolution. A case in point is the so-called love-dart that some terrestrial molluscs shoot at their prospective sexual partners. Given that the likelihood of copulation is not different after solid hits than after complete misses, why do these suitors act so violently towards their chosen mates? Previously, it was shown that successful dart shooting enhances paternity. We conducted an experiment to determine whether the dart achieves its effect by a purely mechanical action or by transferring a bioactive substance. We found that injections of mucus from a gland associated with the dart more than doubled paternity relative to injections of saline. These results support the hypothesis that the dart transfers a substance capable of reconfiguring the spermatophore-receiving organs. While dart shooting probably evolved as the result of sperm competition, a role for cryptic female choice cannot be excluded. Our results imply that if cryptic female choice is operating in this system, it is likely to be based on the properties of the mucus and not on properties of the dart itself. Since we also found evidence of early-male sperm precedence, we conclude that snails can optimize their reproductive success by mating with virgins and shooting their darts accurately.
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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 | 0.000 | 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) | 0.001 | 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".