Linking Changes in Fatty Acid Composition to Postharvest Needle Abscission Resistance in Balsam Fir Trees
Bibliographic record
Abstract
Balsam fir needle retention and fatty acid profile changes due to cold acclimation throughout autumn, but little is known about the relationship between these two phenomena. The objective was to examine differences in FAs in contrasting needle abscission resistant balsam fir genotypes throughout autumn and early winter. Branches from genotypes with low and high needle abscission resistance (NAR) were collected from September to January and analyzed for FA composition. High NAR genotypes retained needles 120–130% longer than low NAR genotypes and NAR increased through autumn in both genotypes. There was approximately a 3:1 ratio of unsaturated: saturated FAs, which increased by 4% in favor of unsaturated fatty acids through autumn. Palmitic, palmitoleic, and linolenic acid content was significantly higher in high NAR versus low NAR genotypes; arachidic, oleic, linoleic, pinolenic, coniferonic, icosadienoic, and sciadonic acids were lower in high NAR genotypes versus low. Linolenic acid was of particular interest because it tended to decrease throughout autumn, to the point that high NAR genotypes were significantly lower in linolenic acid than low NAR genotypes in January. These changes may be linked to an increase in abscisic acid and/or jasmonic acid synthesis depleting linolenic acid stores and promoting postharvest needle abscission resistance.
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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 | 0.000 | 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) | 0.001 | 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".