Anatomical, Developmental and Physiological Bases of Tree-Ring Formation in Relation to Environmental Factors
Bibliographic record
Abstract
Abstract Understanding the process of wood formation and its dynamics over the growing season is fundamental to interpret the isotopic signature of tree rings. Indeed, the isotopic signal recorded in wood does not only depend on the conditions influencing carbon, water, and nitrogen uptake in the leaves and roots, but also on how these elements are translocated to the stem and incorporated into the developing xylem. Depending on environmental conditions, tree developmental stage, and physiological status, wood formation dynamics can vary greatly and produce tree-ring structures carrying specific isotopic signatures. In this chapter, we present the physiological processes involved in wood formation, along with their relationships with anatomical, developmental, and environmental factors, to understand when and how photosynthetic assimilates are progressively incorporated into the forming xylem, creating the final isotopic signature of a tree ring. First, we review current knowledge on the structure and functions of wood. Then we describe the xylogenesis process (how and when the new xylem cells produced by the cambium develop through successive differentiation phases), and its relationships with physiological, developmental, and environmental factors. Finally, we explain the kinetics of xylemcell differentiation and show why the knowledge recently acquired in this field allows us to better understand the isotopic signals in tree rings.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".