Rice Root Aphids, <i>Rhopalosiphum rufiabdominale</i>, Preferentially Choose and Multiply Faster on Monocotyledonous Plants
Bibliographic record
Abstract
ABSTRACT Rice root aphids (RRAs), Rhopalosiphum rufiabdominale (Sasaki) (Hemiptera: Aphididae), reportedly feed on a wide range of monocotyledonous plants (“monocots”) and dicotyledonous plants (“dicots”). However, possible preference for either monocots or dicots, as well as the mechanisms underlying host plant selection, have not yet been investigated. In two‐choice and no‐choice laboratory experiments, we tested whether RRAs (1) select monocots or dicots as host plants, (2) multiply faster on monocots or dicots, and (3) choose preferred host plants based solely on olfactory cues. When RRAs were offered a choice between two potted monocots (rye vs. barley), they showed no preference, but when they were offered a choice between rye and a dicot (cannabis, celery, coriander, lettuce, pepper, squash, tomato, or marigold), they invariably selected and multiplied faster on rye. Similarly, in a no‐choice experiment, where RRAs were confined in a mesh bag fitted with a single host plant, they multiplied equally well on monocots (rye and barley), but significantly less on any of the eight dicots. In moving‐air two‐choice Y‐tube olfactometer bioassays, which presented olfactory but not visual cues of monocots and dicots, the first‐ and final choices of RRAs were mostly indifferent, suggesting that RRAs locate their preferred monocots based not solely on plant odor. As RRAs are emerging pests in commercial cannabis and vegetable production, it is conceivable to use rye as a trap crop to divert RRAs from valuable cannabis and vegetable crops. This concept, however, still requires testing in commercial crop production settings.
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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 | 0.000 | 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".