Environmental and developmental factors driving xylem anatomy and micro‐density in black spruce
Bibliographic record
Abstract
Wood density is the product of carbon allocation for structural growth and reflects the trade-off between mechanical support and water conductivity. We tested a conceptual framework based on the assumption that micro-density depends on direct and indirect relationships with endogenous and exogenous factors. The dynamics of wood formation, including timings and rates of cell division, cell enlargement, and secondary wall deposition, were assessed from microcores collected weekly between 2002 and 2016 from five black spruce stands located along a latitudinal gradient in Quebec, Canada. Cell anatomy and micro-density were recorded by anatomical analyses and X-ray measurements. Our structural equation model explained 80% of micro-density variation within the tree-ring with direct effects of wall thickness (σ = 0.61), cell diameter (σ = -0.51), and photoperiod (σ = -0.26). Wood formation dynamics had an indirect effect on micro-density. Micro-density increased under longer periods of cell-wall deposition and shorter durations of enlargement. Our results fill a critical gap in understanding the relationships underlying micro-density variation in conifers. We demonstrated that short-term responses to environmental variations could be overridden by plastic responses that modulate cell differentiation. Our results point to wood formation dynamics as a reliable predictor of carbon allocation in trees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".