Reproductive investment within inflorescences of Stylidium armeria varies with the strength of early resource commitment
Bibliographic record
Abstract
BACKGROUND AND AIMS: Resource allocation to flowers, fruits and seeds can vary greatly within an inflorescence. For example, distal fruits are often smaller and produce fewer and smaller fruits and seeds than more basal fruits. To assess the causes and functional significance of intra-inflorescence variation, pollen and resources were manipulated to test whether such patterns could be altered within racemes of Stylidum armeria, a perennial Australian herb. METHODS: Pollen and resource levels were manipulated over two flowering seasons. How the number of ovules, fertilized ovules and seeds, the probability of fruit set, and the biomass of floral and fruiting structures varied with their position on the raceme were analysed. KEY RESULTS: Most plants showed a decline in ovule and seed number toward the distal positions on the raceme, but plants differed in their pattern of intra-inflorescence allocation: racemes with greater investment in basal fruits displayed a stronger trade-off with distal investment than did racemes that made smaller initial investments. This trade-off was (a) much stronger for ovule number than for seed number, (b) ameliorated but not erased by resource addition, and (c) exacerbated by resource reduction. There was large and seemingly erratic variation across fruit positions in ovule fertilization and seed set following both natural and supplemental pollination. CONCLUSIONS: In S. armeria, allocation to reproductive traits within the inflorescence is influenced by dynamic trade-offs in resource allocation between early and late fruits, and may also be subject to inherent architectural effects. Large, unpredictable variation among fruits in fertilization success and seed set may influence the evolution of inflorescence size, ovule number and floral dimorphism.
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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".