Getting to the root of carbon reserve dynamics in woody plants: progress, challenges and goals
Bibliographic record
Abstract
Non-structural carbon (NSC) compounds are the primary products of photosynthesis that in their basic forms of glucose and fructose provide the fuel for cell physiological functions. They are also the fundamental building blocks of structural and non-structural primary and secondary metabolites used in processes such as defense, signaling and osmoregulation to adjust for drought and frost conditions. Glucose, fructose and their derivatives are therefore critical components for the maintenance, growth and reproduction of plants, but also for the less-studied processes of their allocation to defense and reserves. While photosynthesis is the primary source of these building blocks, it is important to recognize that the allocation of NSC to the different structures and functions within a plant also includes feedback mechanisms that regulate photosynthesis directly or indirectly (Körner 2003, 2015). As such, NSC should be viewed as an integrator of a multitude of plant physiological processes, such that disentangling the impact of individual processes from each other on NSC remains a challenge. For example, the interpretation of changes in NSC concentrations on their own without the context of a plant’s condition before, during and after stress events is very difficult. Therefore, the need for a better understanding of NSC allocation patterns and mechanisms is crucial in anticipating and potentially predicting how plants, in particular the long-lived species, will respond to current and future abiotic and biotic stresses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".