Modeling eccentric growth explicitly to investigate intra-annual drivers of xylem cell production using xylogenetic data
Bibliographic record
Abstract
Xylogenesis, the process through which wood cells are formed, results in the long-term storage of carbon in woody biomass, making it a key component of the global carbon cycle. Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest. However, studying short-term drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth, i.e., heterogeneous growth around the stem. In this study, we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology, short-term growth rates, and growth eccentricity. To this end, we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions. Our results show that eccentricity generated high temporal autocorrelation between successive samples, and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability. We observed consistent short-term patterns in the model residuals, suggesting the influence of an unaccounted-for environmental variable on cell production. The proposed models offer several advantages over traditional methods, including robust confidence intervals around predictions, consistency with phenology, and reduced sensitivity to extreme observations at the end of the growing season, often linked to eccentric growth. These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".