Metabolic Profiling: A New Tool in the Study of Wood Formation
Bibliographic record
Abstract
In the realm of plant genomics, metabolic profiling has become a valuable tool with which to assess the effect of genetic and/or environmental factors on plant development. This paper reports the first application of metabolic profiling on differentiating xylem tissue of loblolly pine. A protocol is presented for the analysis of loblolly pine xylem tissue. The effects of sample preparation, extraction, and derivatization on the corresponding metabolite profiles and yields have been investigated and are reported. Gas chromatography-mass spectroscopy has been used to quantify >60 polar and lipophilic metabolites from wood-forming tissue. It was possible to assign chemical structures to approximately half of these compounds. Comparison of six loblolly pine genotypes, three high cellulose (50-52%) and three medium (45-48%) cellulose, showed distinct metabolic profiles. Principal component analysis enabled the assignment of metabolic phenotypes using these large data sets. Metabolic phenotype clustering occurred in which the three high-cellulose genotypes were segregated from the medium-cellulose genotypes. These results demonstrate the use of metabolic profiling for the study of wood-forming tissue and as a tool in functional genomics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".