Proximate factors causing mast seeding in <i>Fagus crenata</i>: the effects of resource level and weather cues
Bibliographic record
Abstract
To examine the proximate factors causing mast seeding in Fagus crenata Blume in Hokkaido, northern Japan, we analyzed a 13-year time series of seed production in relation to both previous reproduction and weather conditions. In an autocorrelation analysis we observed a significant negative correlation in 1-year time lags for the log-transformed total seed crop. This indicates that internal resource dynamics are important for mast seeding. A strong negative correlation was observed between the total seed crop and minimum temperature from late April to mid-May in the year preceding flowering. The critical minimum temperature from late April to mid-May for total seed crop at all five sites was about 1.0 °C higher than the 22-year (1979–2000) mean of the minimum temperatures, above which very few seeds were produced. These results show that a weather cue triggers the cessation of reproduction in F. crenata. Regression models that included reproduction in the previous year and minimum temperature explained 57.8%–83.1% of the total seed crop at the five study sites. Therefore, resource dynamics and weather cues are clearly involved in mast seeding in F. crenata.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".