From xylogenesis to tree rings: wood traits to investigate tree response to environmental changes
Bibliographic record
Abstract
ABSTRACT It is noteworthy that the largest part of global vegetation biomass depends on a thin layer of cells: the vascular cambium. Understanding the wood formation processes and relationships with environmental factors is a crucial and timely research question requiring interdisciplinary efforts, also to upscale the information gained and to evaluate implications for tree growth and forest productivity. We provide an overview of wood formation processes up to tree-ring development, bearing in mind that the combined action of intrinsic factors and environmental drivers determines the anatomical traits of a tree ring formed at a specific time and position within the tree’s architecture. After briefly reviewing intrinsic factors, we focus attention on environmental drivers highlighting how a correct interpretation of environmental signals in tree rings must be grounded in a deep knowledge of xylogenesis and consequent wood anatomical traits. We provide guidelines on novel methods and approaches recently developed to study xylem formation. We refer to existing literature on established techniques for retrospective analyses in tree-ring series of anatomical and isotopic traits, to assess long-term ecophysiological responses to environmental variations, also giving advice on possible bias because of interand within-tree variability. Finally, we highlight that, once the temporal axis of intra-annual tree-ring variability of xylem traits is established by xylogenesis analysis, a multidisciplinary approach linking classical dendro-ecology, wood functional traits (dendro-anatomy) and eco-physiology (here focusing on dendro-isotopes) allows a better interpretation of past environmental events hidden in tree rings, and more reliable forecasts of wood growth in response to climate change.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".