A critical thermal transition driving spring phenology of Northern Hemisphere conifers
Bibliographic record
Abstract
Despite growing interest in predicting plant phenological shifts, advanced spring phenology by global climate change remains debated. Evidence documenting either small or large advancement of spring phenology to rising temperature over the spatio-temporal scales implies a potential existence of a thermal threshold in the responses of forests to global warming. We collected a unique data set of xylem cell-wall-thickening onset dates in 20 coniferous species covering a broad mean annual temperature (MAT) gradient (-3.05 to 22.9°C) across the Northern Hemisphere (latitudes 23°-66° N). Along the MAT gradient, we identified a threshold temperature (using segmented regression) of 4.9 ± 1.1°C, above which the response of xylem phenology to rising temperatures significantly decline. This threshold separates the Northern Hemisphere conifers into cold and warm thermal niches, with MAT and spring forcing being the primary drivers for the onset dates (estimated by linear and Bayesian mixed-effect models), respectively. The identified thermal threshold should be integrated into the Earth-System-Models for a better understanding of spring phenology in response to global warming and an improved prediction of global climate-carbon feedbacks.
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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) | 0.001 | 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".