Winter sampling and seasonal variation in litter-dwelling beetle assemblages using a sifting method
Bibliographic record
Abstract
Abstract Litter-dwelling arthropods play an important role in maintaining forest ecosystem function. This study was designed to understand seasonal variations and diversity of litter-dwelling adult beetles, one of the most diverse groups of arthropods. Sampling was conducted in mixed-wood forests of South Korea between March and December 2019, covering all seasons, including winter. We used a sifting method and a Berlese funnel to collect arthropods living in leaf litter and soil. We collected a total of 5820 invertebrates representing six orders, of which 1422 were beetles representing 24 families and minimum 141 species. Beetle species richness was highest in spring and lowest in summer based on rarefaction and extrapolation. However, beetle abundance was lowest in spring, but abundance was similar among the other seasons. Beetle assemblage composition was correlated significantly with soil surface and atmospheric temperature. The assemblage composition differed among seasons, except between spring and winter, which overlapped slightly. The combined sifting–Berlese funnel method showed great advantages for investigating the diversity of overwintering arthropods. Continued study of the relationship between arthropods and the leaf-litter environment is essential to understand this microecosystem and will increase the chance of discovering new beetle species.
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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 | 0.001 | 0.001 |
| 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".