Lacking sensory (rather than locomotive) legs affects locomotion but not food detection in the harvestman<i>Holmbergiana weyenberghi</i>
Bibliographic record
Abstract
The ability to release a leg when forced by predators or during agonistic interactions is widespread and frequent in arthropods. Despite immediate benefits, losing legs may affect locomotion, sensory performance, reproduction, and fitness. The costs of autospasy in arachnids have been scarcely addressed. Therefore, we tested the hypothesis that the number and type of self-amputated legs (sensory or locomotive) affect locomotion and food detection speeds in the harvestman Holmbergiana weyenberghi (Holmberg, 1876) (Sclerosomatidae). With field surveys in a subtropical forest in Uruguay we found that 35% of individuals lacked at least one leg, and sensory legs (second pair) were the most frequently lost. In an indoor setup, we found that individuals missing one sensory leg walked and climbed a trunk slower than individuals lacking a locomotive leg (first, third, or fourth pair), or compared with those with eight legs. Lacking legs did not affect the food detection speed. Additionally, larger individuals with eight legs had greater walking and climbing speeds. Therefore, losing sensory legs affects locomotion in these harvestmen and may confer costs in orientation, balance, and substrate recognition. Finally, we compared our results with the different patterns reported for the effect of autospacy in other harvestman species.
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 | 0.000 |
| 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".