The impact of plant associations on<i>Macrosteles quadrilineatus</i>management in carrots
Bibliographic record
Abstract
Abstract Habitat diversification can influence the interactions of insects with plants and this can be used in agroecosystems for the management of pest populations. Plant diversification can be achieved through planting crops, such as trap crops, or by adjusting weed management. Aster leafhopper,Macrosteles quadrilineatusForbes (Hemiptera: Cicadellidae), is a polyphagous species that uses cereals, vegetables, and weeds as host plants. The influence of weeds onM. quadrilineatusabundance was investigated experimentally in carrot [Daucus carotaL. cv. Canada (Apiaceae)] field plots by adjusting the level of management of two groups of weeds (broadleaf and grass) and by comparing it to weed‐free plots. The preference ofM. quadrilineatusfor different cereal and weed species relative to carrots was tested in choice test assays. Habitat context influenced the abundance ofM. quadrilineatusin the field experiments. The presence of border crops such as oat, rye, barley, wheat, and triticale did not significantly attract or repel this insect to carrot plots compared to the no‐border treatment. However, spelt‐bordered plots had 42% fewerM. quadrilineatusthan three treatments, triticale, wheat, and barley, that had the highest insect abundance. The type of weed management affectedM. quadrilineatusabundance in carrot plots, but not the frequency of herbicide application. Plots that had carrot growing with broadleaf‐weeds had about 59% fewerM. quadrilineatuscompared with those growing with crabgrass or carrot alone. In the greenhouse choice tests, grasses (e.g., cereals) attracted and broadleaf‐weeds repelledM. quadrilineatusrelative to carrots. In summary, carrot growers may be able to manage this pest by reducing the interaction of cereal cover crops with carrots and eliminating grassy weeds in commercial production fields.
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 | 0.001 | 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".