Quantitative methods for defining mast‐seeding years across species and studies
Bibliographic record
Abstract
Abstract: Although there is a quantitative method that is commonly used for identifying mast‐seeding behaviour of a plant population based on the coefficient of variation (i.e. CV is standard deviation/mean>1), there is no general quantitative method for delineating “mast” as opposed to “non‐mast” years. Mast years are, however, described qualitatively as years when “large”, “unusually large” and “high” seed production occurs. The use of a consistent and generally applicable method for delineating mast years across species and plant populations is important for synthesizing knowledge of the causes and consequences of mast seeding, which could be confounded by using different methods among studies. We examine six quantitative methods for identifying mast years: four methods from the literature and two methods developed here. We use 36 seed production datasets covering a variety of species with ≥10 years of data to test the performance of these six methods. For each method, we quantify the percentage of the datasets to which the method could be successfully applied, the magnitude of the mast year relative to the mean, the frequency of mast years and the occurrence of consecutive mast years. The majority of the methods failed to meet the criteria for a suitable method. The best method used the number of standard deviates (standardized deviate method) of the annual mean seed production from the long‐term mean of the dataset to identify mast‐seeding years. General results from the standardized deviate method include that the occurrence of mast‐seeding years is largely unrelated to plant population CV, but similar across species and data collection methods.
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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 | 0.092 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".