Identifying the relevant carbohydrate storage pools available for remobilization in aspen roots
Bibliographic record
Abstract
Nonstructural carbohydrate (NSC) remobilization remains poorly understood in trees. In particular, it remains unclear (i) which tissues (e.g., living bark or xylem) and compounds (sugars or starch) in woody plants are the main sources of remobilized carbon, (ii) to what extent these NSC pools can be depleted and (iii) whether initial NSC mass or concentration is a better predictor of regrowth potential following disturbance. To address these questions, we collected root segments from a large mature trembling aspen stand; we then allowed them to resprout (sucker) in the dark and remobilize NSC until all sprouts had died. We found that initial starch mass, not concentration, was the best predictor of subsequent sprout mass. In total, more NSC mass (~4×) was remobilized from the living inner bark than the xylem of the roots. After resprouting, root starch was generally depleted to <0.6% w/w in both tissues. In contrast, a large portion of sugars appear unavailable for remobilization: sugar concentrations were only reduced to 12% w/w in the bark and 2% in the xylem. These findings suggest that in order to test whether plant processes like resprouting are limited by storage we need to (i) measure storage in the living bark, not just the xylem, (ii) consider storage pool size-not just concentration-and (iii) carefully determine which compounds are actually components of the storage pool.
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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 | 0.001 | 0.001 |
| 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".