Flies and Flowers II: Floral Attractants and Rewards
Bibliographic record
Abstract
This paper comprises Part II of a review of flower visitation and pollination by Diptera (myiophily or myophily). While Part I examined taxonomic diversity of anthophilous flies, here we consider the rewards and attractants used by flowers to procure visits by flies, and their importance in the lives of flies. Food rewards such as pollen and nectar are the primary reasons for flower visits, but there is also a diversity of non-nutritive rewards such as brood sites, shelter, and places of congregation. Floral attractants are the visual and chemical cues used by Diptera to locate flowers and the rewards that they offer, and we show how they act to increase the probability of floral visitation. Lastly, we discuss the various ways in which flowers manipulate the behaviour of flies, deceiving them to visit flowers that do not provide the advertised reward, and how some flies illegitimately remove floral rewards without causing pollination. Our review demonstrates that myiophily is a syndrome corresponding to elements of anatomical, behavioural and physiological adaptations of flower-visiting Diptera. The bewildering diversity of anthophilous Diptera and of the floral attractants and rewards to which they respond allows for only broad generalizations on myiophily and points to the need for more investigation. Ecological relationships between flies and flowers are critical to the survival of each group in many habitats. We require greater understanding of the significance of flies in pollination, especially in the face of recent pollinator declines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".