Identification, measurement and interpretation of tree rings in woody species from mediterranean climates
Bibliographic record
Abstract
We review the literature dealing with mediterranean climate, vegetation, phenology and ecophysiology relevant to the understanding of tree-ring formation in mediterranean regions. Tree rings have been used extensively in temperate regions to reconstruct responses of forests to past environmental changes. In mediterranean regions, studies of tree rings are scarce, despite their potential for understanding and predicting the effects of global change on important ecological processes such as desertification. In mediterranean regions, due to the great spatio-temporal variability of mediterranean environmental conditions, tree rings are sometimes not formed. Often, clear seasonality is lacking, and vegetation activity is not always associated with regular dormancy periods. We present examples of tree-ring morphology of five species (Arbutus unedo, Fraxinus ornus, Quercus cerris, Q. ilex, Q. pubescens) sampled in Tuscany, Italy, focusing on the difficulties we encountered during the dating. We present an interpretation of anomalies found in the wood structure and, more generally, of cambial activity in such environments. Furthermore, we propose a classification of tree-ring formation in mediterranean environments. Mediterranean tree rings can be dated and used for dendrochronological purposes, but great care should be taken in selecting sampling sites, species and sample trees.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".