Ambiente e Salute News n.20 - marzo-aprile 2023
Bibliographic record
Abstract
The European Environment Agency (EEA) has recently developed the European Environment and Health Atlas: an online platform [1] to reveal the air quality where we live, noise levels, green areas, and the quality of bathing sites. It is one of the tools set up by EEA to monitor the quality of the environment in Europe as part of policies to achieve zero pollution. It will be updated regularly and is open to user feedback. It is possible to obtain information on the air quality in our environment by clicking on a map or entering our address, thus obtaining indications of annual averages for PM2.5 , NO2 and Ozone and displaying the concentration of the pollutants as well as the number of people exposed to them, the number of preventable premature deaths and the number of life years lost. There is also the measure of noise monitoring level (not available for Italian locations), bathing water quality and the green area closest to your residence. In this issue of Environment and Health news there are various articles of the effects of air pollution on the vulnerable population (pregnant women and children), so the atlas is an excellent way to learn about the quality of the air we breathe every day in the places where we live and work. In this journal we continue to summarize the main articles published in the monitored journals, all articles and editorials deemed worthy of attention are listed divided by topic, with a brief commentary. This issue is based on the systematic monitoring of publications in March and April 2023.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.492 | 0.449 |
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".