Metabolic dynamics during autumn cold acclimation within and among populations of Sitka spruce (<i>Picea sitchensis</i>)
Bibliographic record
Abstract
• Autumnal cold acclimation in conifers is a complex process, the timing and extent of which vary widely along latitudinal gradients for many tree species and reflect local adaptation to climate. Although previous studies have detailed some aspects of the metabolic remodelling that accompanies cold acclimation in conifers, little is known about global metabolic dynamics, or how these changes vary among phenotypically divergent populations. • Using untargeted GC-MS metabolite profiling, we monitored metabolic dynamics during autumnal cold acclimation in three populations of Sitka spruce from the southern, central, and northern portions of the species range, which differ in both the timing and extent of cold acclimation. • Latitudinal variation was evident in the nature, intensity, and timing of metabolic events. Early development of strong freezing tolerance in the northern population was associated with a transient accumulation of amino acids. By late autumn, metabolic profiles were highly similar between the northern and central populations, whereas profiles for the southern population were relatively distinct. • Our results provide insight into the metabolic architecture of latitudinal adaptive variation in autumn acclimation and show that different mechanisms are the basis of early October cold hardiness and autumn-acclimated cold hardiness.
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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 | 0.000 | 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) | 0.000 | 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".