Insect-flower interactions in the Mediterranean area: a Citizen Science dataset collated within the LIFE 4 Pollinators project
Bibliographic record
Abstract
Pollinators play a vital role in most terrestrial ecosystems, supporting wild plant communities and enhancing agricultural yields. However, despite their ecological and economic importance, they have been experiencing an alarming decline over the past decades. The Mediterranean region, known for harboring highly diverse communities of plants and pollinators, is particularly vulnerable due to intense anthropogenic pressures. Furthermore, the ecological roles of many floral visitors remain poorly understood, hindering conservation efforts. In response, in recent years, growing attention has been directed toward the contribution that citizens can give in support of pollinator research. An increasing number of projects have adopted a Citizen Science approach to enable large-scale data collection. The LIFE 4 Pollinators project (LIFE18/GIE/IT/000755) “Involving people to protect wild bees and other pollinators in the Mediterranean” aims to promote the conservation of pollinating insects and entomophilous plants across the Mediterranean region by fostering progressive changes in human practices that threaten wild pollinators. In addition to the implementation of several actions to raise awareness, the project launched a web platform to collect photographic records of flower–insect interaction from the public. The platform is expected to remain active for at least ten years, during which we encourage continuing record submissions by interested bodies. With this data paper we are making the current dataset freely accessible to anyone, committing to periodic online updates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".