Behavioural responses to potential dispersal cues in two economically important species of cereal-feeding eriophyid mites
Bibliographic record
Abstract
Passively dispersing organisms should optimise the time and direction of dispersal by employing behaviours that increase their probability of being successfully transported by dispersal agents. We rigorously tested whether two agriculturally important passively-dispersing eriophyoid species, wheat curl mite (WCM) and cereal rust mite (CRM), display behaviours indicating their readiness to depart from current host plants in the presence of potential dispersal cues: wind, an insect vector and presence of a fresh plant. Contrary to our expectations, we found that both species decreased their general activity in the presence of wind. When exposed to wind, WCM (but not CRM) significantly increased behaviour that has previously been considered to facilitate dispersal (in this case, standing vertically). Our study provides the first sound test of the function of what have been interpreted as dispersal-related behaviours of eriophyid mites. The low proportion of WCM exhibiting dispersal behaviour suggests there may be predisposed dispersers and residents in the population. Moreover, we found that WCM was generally more active than CRM, which is likely a contributing factor to its high invasive potential.
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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) | 0.001 | 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".