An assessment of temporal variability in mast seeding of North American Pinaceae
Bibliographic record
Abstract
Abstract Our overall objective is to synthesize mast-seeding data on North American Pinaceae to detect characteristic features of reproduction (i.e. development cycle length, serotiny, dispersal agents), and test for patterns in temporal variation based on weather variables. We use a large dataset (n = 286 time series; mean length = 18.9 years) on crop sizes in four conifer genera (Abies, Picea, Pinus, Tsuga) collected between 1960 and 2014. Temporal variability in mast seeding (CVp) for 2 year genera (Abies, Picea, Tsuga) was higher than for Pinus (3 year), and serotinous species had lower CVp than non-serotinous species; there were no relationships of CVp with elevation or latitude. There was no difference in family-wide CVp across four tree regions of North America. Across all genera, July temperature differences between bud initiation and the prior year (ΔT) was more strongly associated with reproduction than absolute temperature. Both CVp and ΔT remained steady over time, while absolute temperature increased by 0.09°C per decade. Our use of the ΔT model included a modification for Pinus, which initiates cone primordia 2 years before seedfall, as opposed to 1 year. These findings have implications for how mast-seeding patterns may change with future increases in temperature, and the adaptive benefits of mast seeding. This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’.
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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 | 0.001 | 0.002 |
| 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".