A longer wood growing season does not lead to higher carbon sequestration
Bibliographic record
Abstract
A reliable assessment of forest carbon sequestration depends on our understanding of wood ecophysiology. Within a forest, trees exhibit different timings and rates of growth during wood formation. However, their relationships with wood anatomical traits remain partially unresolved. This study evaluated the intra-annual individual variability in growth traits in balsam fir [Abies balsamea (L.) Mill.]. We collected wood microcores weekly from April to October 2018 from 27 individuals in Quebec (Canada) and prepared anatomical sections to assess wood formation dynamics and their relationships with the anatomical traits of the wood cells. Xylem developed in a time window ranging from 44 to 118 days, producing between 8 and 79 cells. Trees with larger cell production experienced a longer growing season, with an earlier onset and later ending of wood formation. On average, each additional xylem cell lengthened the growing season by 1 day. Earlywood production explained 95% of the variability in xylem production. More productive individuals generated a higher proportion of earlywood and cells with larger sizes. Trees with a longer growing season produced more cells but not more biomass in the wood. Lengthening the growing season driven by climate change may not lead to enhanced carbon sequestration from wood production.
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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) | 0.002 | 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".