18. Comparing Relationships between Plant Body Sizes and Minimum Reproductive Threshold Sizes for Species of Contrasting Life History
Bibliographic record
Abstract
Two traits are fundamental in defining plant life history strategies: How big can a species get? And how big does it need to get before it can reproduce? Previous research has shown that there is a general positive relationship between these two traits, across species, and this can be accounted for as a trade-off. In this project, I explored whether this relationship differs among herbaceous species with perennial versus annual or biennial life histories. Perennials, because of their capacity to grow across several years, might generally be expected to display a relatively large MAX (maximum potential body size) and hence large MIN (minimum reproductive threshold size) compared with annuals or biennials that live only one or two years. In addition, annuals/biennials might be expected generally to have a smaller MIN for a given MAX, compared with perennials because of selection in the ancestral past — i.e. in frequently disturbed habitats, where annuals and biennials are common, predictable early death (from disturbance) has imposed strong selection to produce at least some offspring quickly, regardless of how small/suppressed the plant might be. I tested these predictions for resident plants sampled from natural populations of 105 species found in the vicinity of Kingston, Ontario. Remarkably, the results support neither prediction, and point to an alternative consequence of selection in shaping plant life history strategies; i.e. small MIN for a given MAX has also been favoured in perennials but for a different reason — as a strategy for competitive fitness.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".