Tallow tree allocates contrasting secondary chemicals in response to varying environments along elevational gradients
Bibliographic record
Abstract
Abstract Aims Understanding how tree species regulate multiple types of secondary chemicals along elevational gradients is critical for elucidating the physiological and ecological strategies of plants in response to varying biotic and/or abiotic environments. This study aims to examine how Chinese tallow tree (Triadica sebifera) allocates resources to the production of different secondary chemicals in response to varying environments across elevational gradients. Methods We conducted field surveys of different herbivore feeding guilds and their damage rates on Chinese tallow trees along an elevational gradient in China and measured secondary chemicals (tannins and flavonoids) in damaged and undamaged leaves. Important Findings The odds of a leaf being damaged (chewing or scarring) decreased with elevation. Flavonoid concentrations increased with elevation in undamaged leaves but decreased with elevation in damaged leaves, with quercitrin contributing most strongly to this pattern, likely as results of plant responding to changing biotic or abiotic stresses along elevational gradients. Tannin concentrations did not vary with elevation, so undamaged leaves had relatively lower tannin to flavonoid ratios at high elevation than at low elevation. Our study reveals variation in herbivory and contrasting trends in plant secondary metabolism along an elevation gradient and highlights the importance of simultaneously considering multiple types of secondary chemicals in plant physiological and ecological strategies.
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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 | 0.001 | 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".