Passively crowdsourcing images online for measuring broad-scale fly (Diptera) floral interactions and biodiversity
Bibliographic record
Abstract
Flies (Diptera) represent one of the largest and most important groups of pollinators on the planet; however, little is known about the interactions between flies and flowers compared to well-known pollinators, such as bees. Understanding pollinator assemblages is key to conserving biodiversity and ecosystem services, but monitoring Diptera is time and cost intensive. Using photographs of blooming flowers taken by photographers worldwide and uploaded on internet repositories, we built a dataset of 1,275 images of fly-flower visitations and extracted fly and flower taxonomic information, flower characteristics (shape and color), and fly activity (pollen carrying and foraging). The resulting dataset shows taxonomic and other biases but can still provide an initial overview of factors that affect pollination by Diptera. We identified 22 families of flies, with blow flies (Family Calliphoridae) most represented (29%) and 63 families of flowers, with Asteraceae (42%) and Apiaceae (21%) as the most common. Using logistic regression, we found that the likelihood of flies carrying pollen was determined by the interaction between flower color and shape: pollen-carrying was more likely when elongate cluster flowers were green-yellow. Fly foraging on flowers was determined by flower color: flies were more likely to feed on green-yellow and white flowers. Overall, Syrphidae flies were less likely to forage for nectar than non-Syrphidae, but were more likely to carry pollen. While biases exist in crowdsourced data, we show that data from photographs collected through citizen science offers potentially valuable information for monitoring pollinator-flower interactions and augment our understanding of pollinator ecology in an era of global insect 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".