Behavioural responses of pollinators to variation in floral display size and their influences on the evolution of floral traits
Bibliographic record
Abstract
The number of flowers open at any one time on a plant, i.e., floral display size, varies greatly among plant species. For example, some species flower during a brief period and have many open flowers, while others have extended flowering with only a few open flowers at one time (Gentry 1974; Bawa 1983). Also, floral display size often varies among individuals of the same plant species (e.g., Willson & Price 1977; Pleasants & Zimmerman 1990). The causes of such variations in floral display size are enduring interest to plant ecologists (reviewed by de Jong et al . 1992). Numerous studies have reported that variation in floral display size produces marked alterations in pollinator behavior. Especially, two types of pollinator response to increased floral display size have been recognized from the perspective of their influences on pollen dispersal. First, larger floral displays attract more pollinators per unit of time (Fig 14.1A; reviewed by Ohashi & Yahara 1998). This will promote cross-pollination in terms of increased pollen receipt, removal, or potential mate diversity (Harder & Barrett 1996). Second, the number of flowers that individual pollinators probe per plant also increases with floral display size (Fig. 14.1B; also reviewed by Ohashi & Yahara 1998). This will increase self-pollination among flowers on the same plant (“geitonogamy”; Richards 1986; de Jong et al . 1993). Thus, variation in floral display size may lead to a substantial difference in pollen dispersal and, in turn, plant 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".