Metabolite variation in ecologically diverse black cottonwood, Populus trichocarpa Torr. & A. Gray
Bibliographic record
Abstract
Black cottonwood (Populus trichocarpa Torr. & A. Gray) is mass productive tree species native to the Pacific Northwest of North America. Gas chromatography - mass spectrometry was used to study the metabolic profiling of leaves from multiple genotypes to investigate the presence of clinal trends in metabolite levels and to determine if relationships with geo-climatic variables and date of bud set exist. In the late summer (September 3rd) of 2008, young leaves were collected from the species’ range and represented by 106 clones grown in a common garden established in Vancouver, British Columbia, Canada. The results validity was verified through the use of two independent canonical correlation analyses (CCA) that were performed on the intensity of the detected 104 compounds, including 40 known metabolites. Principle Component Analysis (PCA) was performed for original variables reduction and to determine the principle components accounting for most of the variation (the first ten PCAs accounted for 63% of the variation). The first analysis utilized the metabolites associated with the first ten principal components to determine the relationship between the original metabolites and geography, climate and date of bud set, while the second was based on the first ten principal components themselves. Both analyses yielded strong to moderate trends but the correlations (ranging from 0.45 to 0.97) were not statistically significant most likely due to the small sample size used. Based on the analyses conducted, it appears that P. trichocarpa ecotypes are preconditioned to suite their location-origin and the observed differences in metabolites reflected the genotypic variability among the studied trees.
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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) | 0.000 | 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 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".