Masting behaviour in beech: linking reproduction and climatic variation
Bibliographic record
Abstract
The question of what triggers masting in beech (Fagus) has been a source of uncertainty and curiosity. Analysing seed production series from Europe (Fagus sylvatica L.), eastern North America (Fagus grandifolia Ehrh.), and Japan (Fagus crenata Blume), for various periods (lasting between 6 and 34 years) over the last 150 years, we find a close relationship between masting (mast year) and preceding growing season climate events (mast year 1 and mast year 2 ) in eastern North America and Europe, with tentative indications of this pattern in Japan. A drought in the early summer preceding masting (mast year 1 ) is a very strong predictor in Europe and eastern North America, but drought events were not found for the Japan series. The predictive power is increased in all three regions if there has been an unusually moist, cool summer the year before the drought (mast year 2 ). We suggest that, in this initial moist summer (mast year 2 ), carbohydrate buildup within the trees "primes" them for floral induction the following year (year 1 ). In the European and eastern North American series, a drought event in the early part of the following summer (mast year 1 ) acts as a proximal trigger for the release of those reserves into flower initiation and then seed production.Key words: masting, Fagus spp., floral induction, drought, climatic variation, evolutionary ecology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".