Comparing the Cell Dynamics of Tree-Ring Formation Observed in Microcores and as Predicted by the Vaganov–Shashkin Model
Bibliographic record
Abstract
New insights into the intra-annual dynamics of tree-ring formation can improve our understanding of tree-growth response to environmental conditions at high-resolution time scales. Obtaining this information requires, however, a weekly monitoring of wood formation, sampling that is extremely time-intensive and scarcely feasible over vast areas. Estimating the timing of cambial and xylem differentiation by modeling thus represents an interesting alternative for obtaining this important information by other means. Temporal dynamics of cambial cells can be extracted from the daily tree-ring growth rate computed by the Vaganov–Shashkin (VS) simulation model, assuming that cell production is tightly linked to tree-ring growth. Nonetheless, these predictions have yet to be compared with direct observations of wood development, i.e., via microcoring, over a long time span. We tested the performance of the VS model by comparing the observed and predicted timing of wood formation in black spruce [Picea mariana (Mill.)]. We obtained microcores over 15 years at 5 sites along a latitudinal gradient in Quebec (Canada). The measured variables included cell size and the timing of cell production and differentiation. We calibrated the VS model using daily temperature and precipitation recorded by weather stations located on each site. The predicted and observed timing of cambial and enlarging cells were highly correlated (R2 = 0.8); nonetheless, we detected a systematic overestimation in the predicted timing of cambial cells, with predictions delayed by 1–20 days compared with observations. The growth rate of cell diameter was correlated with the predicted growth rate assigned to each cambial cell, confirming that cell diameter developmental dynamics have the potential to be inferred by the tree-ring growth curve of the VS model. The mismatch between the observed and predicted timing of wood formation in black spruce within our study area can be reduced by better adapting the VS model to wet sites, a context for which this model has been rarely used. Integrating new knowledge related to the temporal dynamics of cell development into the model is also required to obtain more reliable predictions of the timing of xylem differentiation.
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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 | 0.001 |
| 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 | 0.001 | 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".