The effect of variation among floral traits on the flower constancy of pollinators
Bibliographic record
Abstract
The interaction between floral traits and pollinator behavior has been an important force in the coevolution of plants and their animal pollinators. An element of conflict underlies this interaction because the ideal behavior of the pollinator from the plant's point of view may often diverge from that dictated by the pollinator's own self-interest. Because of their immobility, outcrossed plants require a reliable courier that has a high probability of placing their pollen where it has a chance of fertilizing a conspecific ovule. Pollen finding an inappropriate stigma is effectively wasted, and deposition of heterospecific pollen may block receptive sites on the stigma and reduce seed set (e.g., Waser 1978, 1983; Thomson et al. 1981; Campbell & Motten 1985). Thus, plants should benefit if pollinators tend to move sequentially among flowers of the same species, a pattern that an optimally foraging pollinator should rarely adopt unless energetic returns from one plant species regularly exceed those from a mixed diet of some or all of the flower species available. More often, pollinators distribute themselves in an ideal free pattern across resources (Dreisig 1995), thereby minimizing differences in rewards among many different plant species, a pattern that should make generalist foraging the best option. Yet pollinators often sequentially visit the flowers of one species even though they are bypassing flowers of other available, rewarding plant species (e.g., Grant 1950; Manning 1957; Free 1970; Waser 1983, 1986; Lewis 1989; Goulson & Cory 1993; Laverty 1994 b ).
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".