Dynamical systems for plant carbon storage: describing complex reserve dynamics from simple fluctuations in photosynthesis and carbon allocation
Bibliographic record
Abstract
This scientific commentary refers to ‘Modeling starch dynamics from seasonal variations of photosynthesis, growth and respiration’ by Oswald and Aubrey (doi: 10.1093/treephys/tpad007). The assimilation of carbon through photosynthesis can vary considerably throughout the year (Dietze et al. 2014). To survive, trees must form an energetic buffer in the form of non-structural carbohydrates (NSCs, i.e., soluble sugars, starch, lipids, hemicellulose and sugar alcohols; Gibon et al. 2009, Signori-müller et al. 2021). While the size and seasonal amplitude of this buffer are known to vary considerably among climates and species, most models of tree NSC dynamics still use simple allometric scaling ratios (Franklin et al. 2012, Furze et al. 2019, Fermaniuk et al. 2021). This static treatment of carbon allocation may explain why most vegetation models likely underestimate allocation to NSCs (Würth et al. 2005, Franklin et al. 2012). In this special issue of Tree Physiology, Oswald and Aubrey (2023) emphasize the role of non-structural carbohydrates (NSCs) as a central axis of a tree’s carbon balance, pointing the way forward for future vegetation models to incorporate allocation between NSC reserves and growth as a dynamic process rather than a fixed fraction of photosynthesis.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".