Intraspecific variability of monoterpene composition emitted by <i>Quercus ilex</i> leaves
Bibliographic record
Abstract
Intraspecific variation in the composition of monoterpenes emitted from Quercus ilex L. leaves was investigated in southern France by screening the emissions of 146 trees and by examining the effects of season, age, and crown position of leaves on the composition of emissions. All trees emitted the same blend of monoterpenes, mainly α-pinene, β-pinene, sabinene, myrcene, and limonene but in distinctly different proportions. Of the surveyed trees, 71% emitted mainly pinenes (6080%), 21% had higher proportions of limonene (3080%), and 8% had higher proportions of myrcene (6090%) in their emissions. For a given tree, the proportions were similar for leaves of different ages and different crown positions, and remained unchanged throughout different seasons.
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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 | 0.001 | 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) | 0.000 | 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 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".