Short‐Term Grasslands in Agriculture Support Both Natural Enemy and Phytophagous Arthropod Populations
Bibliographic record
Abstract
ABSTRACT Simplified agricultural land with high chemical input is increasingly replacing natural habitats in many parts of the world. This loss and fragmentation of natural areas may have profound impacts on an array of wildlife, including predatory arthropods that provide natural biocontrol of agricultural pests. There is increasing interest in utilising short‐term seminatural habitat restoration in agricultural systems to support populations of beneficial organisms. We assessed the impact of two types of short‐term set‐asides on the diversity and abundance/activity density of phytophagous and predatory arthropods. We found that flower‐enhanced set‐asides supported higher abundances of both phytophagous and predatory arthropods compared to control crop fields. In addition, we found that both set‐aside types (flower enhanced and traditional) supported a more active and diverse community of beneficial predatory carabid beetles, with particularly strong trends in set‐asides supplemented with flowering plants. These results suggest that small‐scale, temporary, seminatural habitats in agricultural systems provide support for a diverse and abundant community of both beneficial and phytophagous arthropods.
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) | 0.002 | 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".