Response Factor Considerations for the Quantitative Analysis of Western Redcedar (Thuja plicata) Foliar Monoterpenes
Bibliographic record
Abstract
A method is described for quantitative analysis of monoterpenes in western redcedar (Thuja plicata) foliage by gas chromatography with flame ionization detection. Response factors for monoterpenes identified in redcedar are evaluated to determine similarities among monoterpene responses. Evaluation demonstrates that redcedar monoterpenes yield detector responses that fall into two groups. One monoterpene from each group is used as a standard for quantitative analysis. Redcedar monoterpenes are quantitated by comparing analyte response with the response factor of one of the standards in single-point calibrations. Homogenized foliage samples are extracted with ethyl acetate and the extracts passed through a solid phase extraction column of graphitized carbon to remove plant pigments. Method bias and repeatability are evaluated by fortifying foliage samples with (1S)-(+)-carvone and (1S)-(+)-2-carene and subjecting the samples to the extraction and analysis procedures. Detection limits are also assessed from fortified samples. Excellent recovery (> 95.0%) and precision (< 5%) are obtained from the analysis of 2-carene from fortified samples. Carvone recovery is approximately 80% with excellent precision (< 4%). The method limits of detection obtained from 2-carene and carvone fortified samples are 4.7 and 13.5 microg/g, respectively.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".