The relationship between offspring size and performance in the wolf spider Hogna helluo (Araneae: Lycosidae)
Bibliographic record
Abstract
Life-history theory predicts a trade-off between number of offspring and investment (size) per offspring. An important component of this trade-off is how offspring size influences performance and survival. In this study, we examined the relationships between maternal size, offspring size and clutch size, as well as the relationship between offspring size and performance, in the wolf spider, Hogna helluo. Offspring dispersing from field-collected female Hogna helluo with egg sacs were counted and their carapace width was measured. The relationships between feeding performance (number of prey captured), starvation tolerance and offspring size were examined to determine if offspring size was correlated with offspring performance. Clutch size increased with female size, but there was little evidence for a trade-off between offspring size and number. Starvation tolerance and feeding performance were positively related to offspring size. Our results show that offspring performance increases with offspring size and are consistent with the hypothesis that parental fitness is maximized by producing as many offspring as possible given constraints on a minimum viable offspring size.
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 | 0.000 | 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".