A Comparison of Specimen Extraction Efficiency Using Rapid Rinse and Direct Search in Sweep‐Net Samples for Quantitative Analyses
Bibliographic record
Abstract
ABSTRACT Sweep‐netting is widely used for sampling terrestrial arthropods, but specimen extraction is a sensitive step that can introduce biases and variability in ecological or quantitative investigations. Field storage of multiple, independent sweep‐net samples, is also usually unfeasible because they need to be chilled, or the specimens immediately transferred to alcohol, to avoid both damage and decomposition. Current extraction methods based on washing and sieving of pooled samples are aimed at qualitative surveys, raising the question of whether direct search, also often used, could be more efficient and reliable for quantitative investigations. This study presents an objective comparison of both methods, investigating Hymenoptera extraction efficiency using direct search versus rapid rinse, a field‐adapted variant of traditional rinsing methods designed for use with minimal equipment. Rapid rinse improved specimen recovery by 29%–83% per sample, with only 0.0%–7.6% of specimens missed, compared to 16%–66% missed with direct search. It also yielded 11%–44% more species per sample and reduced processing time by more than half, while maintaining specimen integrity and enabling efficient, stable field storage. Rinsing is therefore recommended as the most reliable and operationally practical extraction method for studies requiring quantitative and comparative accuracy. Rapid rinse adaptations for fieldwork, using just a few items and no assembly, while also resulting in easy storage, improved the technique's overall accessibility.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".