Effects of different methods of non‐lethal tissue sampling on butterflies
Bibliographic record
Abstract
1. We investigated the effects of two methods of non‐lethal tissue sampling on post‐release flight behaviour (short‐term response) and survival (long‐term response) of two butterflies, Pieris rapae and Coenonympha tullia , within the same natural habitat. We applied three treatments: control (no tissue removal), wing clipping, and leg removal. Our study is the first to directly compare the effects of these common sampling methods. 2. We monitored the flight behaviour of the butterflies by following individuals immediately after their release. In 99 behaviour trials of P. rapae and 101 of C. tullia we found no significant differences in proportion of time spent flying or displacement per unit time among treatment groups in either species. 3. We used standard mark–recapture techniques continuously throughout the flight season to compare the survival of individuals. We marked a total of 687 P. rapae and 490 C. tullia butterflies. We found no significant differences in survival among treatments in either species. 4. We detected differences between the sexes in survival in P. rapae and flight behaviour in C. tullia . In addition to indicating differences in ecology between the sexes, these results also suggest that our analyses were sufficiently powerful to detect a significant effect of tissue removal had such an effect existed. 5. Our work is an important addition to the accumulating evidence that these methods of non‐lethal tissue sampling are generally not detrimental. These sampling techniques closely mimic conditions in the wild, as wing wear and leg losses occur naturally.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".