Upscaling xylem phenology: sample size matters
Bibliographic record
Abstract
BACKGROUND AND AIMS: Upscaling carbon allocation requires knowledge of the variability at the scales at which data are collected and applied. Trees exhibit different growth rates and timings of wood formation. However, the factors explaining these differences remain undetermined, making samplings and estimations of the growth dynamics a complicated task, habitually based on technical rather than statistical reasons. This study explored the variability in xylem phenology among 159 balsam firs [Abies balsamea (L.) Mill.]. METHODS: Wood microcores were collected weekly from April to October 2018 in a natural stand in Quebec, Canada, to detect cambial activity and wood formation timings. We tested spatial autocorrelation, tree size and cell production rates as explanatory variables of xylem phenology. We assessed sample size and margin of error for wood phenology assessment at different confidence levels. KEY RESULTS: Xylem formation lasted between 40 and 110 d, producing between 12 and 93 cells. No effect of spatial proximity or size of individuals was detected on the timings of xylem phenology. Trees with larger cell production rates showed a longer growing season, starting xylem differentiation earlier and ending later. A sample size of 23 trees produced estimates of xylem phenology at a confidence level of 95 % with a margin of error of 1 week. CONCLUSIONS: This study highlighted the high variability in the timings of wood formation among trees within an area of 1 km2. The correlation between the number of new xylem cells and the growing season length suggests a close connection between the processes of wood formation and carbon sequestration. However, the causes of the observed differences in xylem phenology remain partially unresolved. We point out the need to carefully consider sample size when assessing xylem phenology to explore the reasons underlying this variability and to allow reliable upscaling of carbon allocation in forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.075 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".