Leaf compositional differences predict variation in <i>Hypsipyla robusta</i> damage to <i>Toona ciliata</i> in field trials
Bibliographic record
Abstract
Hypsipyla robusta Moore is a shoot-boring moth that feeds on species in the Swietenioideae subfamily of Meliaceae, including the rain forest tree Toona ciliata M. Roemer. Damage from Hypsipyla has been a major barrier to growing these species in plantations. Although there has been speculation regarding the role of plant chemistry in determining host selection by Hypsipyla, there is no substantial evidence to support a role for any particular class of compounds. In this study, we used near-infrared spectroscopy (NIRS) to quantify variation in tissue composition to determine whether compositional variation could be linked with differences in H. robusta damage in a sample of 153 T. ciliata tree stems. We found that a discriminant analysis using NIRS data successfully classified most leaflets into high- and low-damage classes. Regression models based on NIRS data were also able to predict variation in leaflet nitrogen and tree height. Taller specimens of T. ciliata were more frequently damaged. Leaf nitrogen varied only a little, making it a weak explanatory variable for insect attack. The capacity of NIRS to predict variation in H. robusta attack suggests a link between T. ciliata leaf chemistry and H. robusta behaviour.
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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 | 0.001 |
| 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".