Modeled Tracheidograms Disclose Drought Influence on Pinus sylvestris Tree-Rings Structure From Siberian Forest-Steppe
Bibliographic record
Abstract
Wood formation allows trees to adjust in a changing climate. Understanding what determine its adjustment is crucial to evaluate impacts of climatic changes on trees and forests growth. Despite efforts to characterize wood formation, little is known on its impact on the xylem cellular structure. In this study we apply the Vaganov-Shashkin model to generate synthetic tracheidograms and verify its use to investigate the formation of intra-annual density fluctuations (IADF), one of the most frequent climate tree-ring markers in drought-exposed sites. Results indicate that the model can produce realistic tracheidograms, except for narrow rings (<1 mm), when cambial activity stops due to an excess of drought or a lack of growth vigor. These observations suggest that IADFs are caused by a release of drought limitation to cells formation in the first half of the growing season, but that narrow rings are indicators of an even more extreme and persistent water stress. Taking the example of IADFs formation, this study demonstrated that the Vaganov-Shashkin model is a useful tool to study the climatic impact on tree-ring structures. The ability to produce synthetic tracheidogram represents an unavoidable step to link climate to tree growth and xylem functioning under future scenarios.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".