Pitfall trap designs to maximize invertebrate captures and minimize captures of nontarget vertebrates
Bibliographic record
Abstract
Abstract Pitfall traps containing a preservative have become the standard method of sampling for epigeal invertebrates such as carabid beetles and cursorial spiders. However, they often result in high levels of mortality for small mammals and amphibians. We compared the carabid, spider, and vertebrate captures within five pitfall trap types (conventional trap, funnel trap, shallow trap, Nordlander trap, and the ramp trap) to determine the trap type that would reduce vertebrate incidental catch without compromising the capture of invertebrates. We also examined the effect of a mesh screen over pitfall traps on carabid beetle and vertebrate catches. All modifications to the conventional trap design resulted in a reduction in both small mammal and amphibian captures. The shallow pitfall trap and the funnel trap captured a carabid beetle and spider fauna similar to that captured by the conventional trap. The species compositions of the ramp trap and the Nordlander trap were different from those of the other trap types, but these traps were more efficient, capturing more species per individual captured. The ramp trap appeared to be the method of choice for sampling epigeal spiders. Thus, the choice among trap designs for invertebrates depends on the objectives of the study. However, an alternative to the conventional trap design should always be considered to reduce small mammal mortality.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".