Causes of spatial patterns of fruit set in waratah: Temporal vs. spatial interactions between flowers on an inflorescence
Bibliographic record
Abstract
Abstract Spatial patterns of fruit set within inflorescences may be controlled by pollination, nutrient allocation, or inflorescence architecture. Generally, flowers that have spatial and/or temporal precedence are more likely to set fruits. We sought to separate these factors by comparing patterns of fruit set on inflorescences of two species ofTelopea(Proteaceae); one that flowers from the tip to the base of the rachis, the other from base to tip. In both species, most fruits were set at the top of the inflorescence (the last flowers to open forT. speciosissima) and this was extreme forT. mongaensis, where the top flowers open first. Fruit set was not generally limited by inadequate pollination for eitherT. mongaensisorT. speciosissima, as hand pollinations did not increase fruit set and many abscised flowers contained pollen tubes. InT. speciosissima, we tested whether removal of developing topmost fruits would ‘release’ those that had initiated but not yet aborted lower down. There was no significant effect. Plant hormones can increase the degree to which a developing fruit is a sink for nutrients, so we applied cytokinin to the developing lower fruits on some inflorescences. There was no significant effect of the hormone treatment. We conclude that temporal precedence may contribute to the skewed pattern of fruit set inT. mongaensis, because there was an extreme concentration of fruit set on the distal part of the inflorescences, but it cannot explain this pattern of fruit set inT. speciosissima, where the distal flowers are the last to open. Some other process must therefore constrain fruit set to the topmost flowers in an inflorescence. While cytokinin application had no significant effect, the power of this experiment was low and we consider that the hypothesis of hormonal control is worth further exploration.
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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".