Cone allometry and seed protection from fire are similar in serotinous and nonserotinous conifers
Bibliographic record
Abstract
Serotiny is an adaptive trait that allows certain woody plants to persist in stand-replacing fire regimes. However, the mechanisms by which serotinous cones avoid seed necrosis and nonserotinous species persist in landscapes with short fire cycles and serotinous competitors remain poorly understood. To investigate whether ovulate cone traits that enhance seed survival differ between serotinous and nonserotinous species, we examined cone traits in 24 species within Pinaceae and Cupressaceae based on physical measurements and cone heating simulations using a computational fluid dynamics model. Fire-relevant cone traits were largely similar between cone types; those that differed (e.g. density and moisture) conferred little seed survival advantage under simulated fire. The most important traits influencing seed survival were cone size and seed depth within the cone, which was found to be an allometric function of cone mass for both cone types. Thus, nonserotinous cones should not suffer significantly greater seed necrosis than serotinous cones of equal size. Closed nonserotinous cones containing mature seeds may achieve substantial regeneration after fire if they are sufficiently large relative to fire duration and temperature. To our knowledge, this is the most comprehensive study of the effects of fire-relevant cone traits on conifer regeneration supported by physics-based fire simulation.
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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".