Pattern of xylem phenology in conifers of cold ecosystems at the Northern Hemisphere
Bibliographic record
Abstract
Abstract The interaction between xylem phenology and climate assesses forest growth and productivity and carbon storage across biomes under changing environmental conditions. We tested the hypothesis that patterns of wood formation are maintained unaltered despite the temperature changes across cold ecosystems. Wood microcores were collected weekly or biweekly throughout the growing season for periods varying between 1 and 13 years during 1998–2014 and cut in transverse sections for assessing the onset and ending of the phases of xylem differentiation. The data set represented 1321 trees belonging to 10 conifer species from 39 sites in the Northern Hemisphere and covering an interval of mean annual temperature exceeding 14 K. The phenological events and mean annual temperature of the sites were related linearly, with spring and autumnal events being separated by constant intervals across the range of temperature analysed. At increasing temperature, first enlarging, wall‐thickening and mature tracheids appeared earlier, and last enlarging and wall‐thickening tracheids occurred later. Overall, the period of wood formation lengthened linearly with the mean annual temperature, from 83.7 days at −2 °C to 178.1 days at 12 °C, at a rate of 6.5 days °C −1 . April–May temperatures produced the best models predicting the dates of wood formation. Our findings demonstrated the uniformity of the process of wood formation and the importance of the environmental conditions occurring at the time of growth resumption. Under warming scenarios, the period of wood formation might lengthen synchronously in the cold biomes of the Northern Hemisphere.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".