From division to ‘divergence’: to understand wood growth across timescales, we need to (learn to) manipulate it
Bibliographic record
Abstract
Wood formation is the Rosetta stone of tree physiology: a traceable, integrated record of physiological and morphological status. It also produces a large and persistent annual sink for terrestrial carbon, motivating predictive understanding. Xylogenesis studies have greatly expanded our knowledge of the intra-annual controls on wood formation, while dendroecology has quantified the environmental drivers of multi-annual variability. But these fields operate on different timescales, making it challenging to predict how short (e.g. turgor) and long timescale processes (e.g. disturbance) interactively influence wood formation. Toward this challenge, wood growth responses to natural climate events provide useful but incomplete explanations of tree growth variability. By contrast, direct manipulations of the tree vascular system have yielded unexpected insights, particularly outside of model species like boreal conifers, but they remain underutilized. To improve prediction of global wood formation, we argue for a new generation of experimental manipulations of wood growth across seasons, species, and ecosystems. Such manipulations should expand inference to diverse forests and capture inter- and intra-specific differences in wood growth. We summarize the endogenous and exogenous factors influencing wood formation to guide future experimental design and hypotheses. We highlight key opportunities for manipulative studies integrating measurements from xylogenesis, dendroanatomy, dendroecology, and ecophysiology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".