Bibliographic record
Abstract
Honeybees have been used in Europe as environmental bioindicators for heavy metals and polycyclic aromatic hydrocarbons (PAHs). However, their potential has been little explored in North America, especially between environments which have similar pollution levels. Many urban residents and stakeholders are concerned with air quality, mainly in regard to gradients of exposure to industrial pollution between deprived and privileged subpopulation. Thus, the aim of this study was to evaluate the use of honeybees as bioindicators to assess exposure to heavy metals and PAHs in Québec City, Canada, in different socioeconomic districts of Quebec City (deprivation index). Honeybees were sampled over a 5-month period (May to September) at six locations distributed in two urban areas that are distinct geomorphologically and socioeconomically (lower town socio-economically deprived and upper town socioeconomically privileged) and two control rural locations. Six PAHs were analyzed by ultra-performance liquid chromatography (UPLC), while four heavy metals were analyzed by inductively coupled plasma mass spectrometry. Arsenic was the only measured pollutant that showed a significant gradient of exposure between rural and urban environments, but also between the two urban areas. Furthermore, we were able to detect significant differences at certain sampling times for heavy metals and PAHs. Overall, the results show that honeybees are sensitive enough to detect differences between the differential urban environments of a city presumed to have similar pollution levels and therefore could be used when potential socio-environmental inequalities are present.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".