Effects of wind speed on background herbivory of an insect herbivore
Bibliographic record
Abstract
Climate change has major effects on background insect herbivory, but only a few studies have involved long-term investigation, and the effects of wind on bigger insect herbivores such as moths have been largely neglected. We correlated climatic data during the period 2006–2017 with a set of background herbivory data of Culcula panterinaria (Bremer et Grey) derived from a long-term investigation (2007–2017) in the oak forest of Luanchuan county, Henan, China and discuss the impacts of wind speed on background insect herbivory. Background insect herbivory was significantly correlated with wind speed parameters of both the same year and previous year, implying direct and cumulative effects of wind speed on background insect herbivory. Our study offers a new perspective for monitoring and predicting background insect herbivory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 teacher head, 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".