The advantage of early flowering in the spring ephemeral annual plant <i>Floerkea proserpinacoides</i>
Bibliographic record
Abstract
• Internal conflicts in the allocation of resources lead to trade-offs or compromises that can influence fitness and the evolution of specific life-history traits. • Here, the reproductive schedule of Floerkea proserpinacoides plants, a spring ephemeral annual of the deciduous forests of eastern North America, was manipulated to determine the presence of potential conflicts between growth and reproduction, and the advantage of early reproduction. • Plants on which flowering was delayed had a greater relative growth rate (RGR) and vegetative biomass than control plants after 8 wks (when control plants started to senesce). Delayed-flowering plants took 1 week longer to senesce, which allowed them to mature almost as many seeds as did control plants in 8 wks. • However, late flowering in F. proserpinacoides may be maladaptive. With reduced light availability through canopy closure, early flowering plants can rapidly divert most of the resources accumulated in the stem and leaves to seed maturation. Thus, the advantage of maintaining a higher RGR by delaying flowering is associated with the greater disadvantage of necessitating more time to mature seeds.
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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".