Impacts of leg loss and regeneration on body condition, growth, and development time in the wolf spider <i>Schizocosa ocreata</i>
Bibliographic record
Abstract
Autotomy (self-amputation) of appendages and subsequent regeneration is common to many taxa. These processes are known to affect foraging abilities, growth, and development time in many taxa. However, little is known about their effects in arachnids. We addressed the effects of autotomy and regeneration on body condition, growth (size and mass), and development time (molt interval) for the wolf spider Schizocosa ocreata (Hentz, 1844) in the field and laboratory. Frequency of autotomy in the field was high (11%–19%). Field-caught individuals with missing or regenerating legs had significantly lower body size, mass, and condition. To test the effects of regeneration on size, mass, and molt interval in the laboratory, we induced autotomy of one or both forelegs. Spiders regenerating two legs had reduced molt intervals, were smaller, and weighed less than spiders that were intact or regenerating one leg. Field-caught spiders that had undergone autotomy and regeneration in the laboratory exhibited reduced size, mass, and molt interval. In contrast, laboratory-reared spiders exhibited increased molt intervals but no difference in mass after regeneration. These results reveal that limb loss via autotomy is common (but potentially costly) in S. ocreata, and that environmentally mediated trade-offs between growth and development time may occur during regeneration.
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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".