Preface to Special Issue - Observation, Control Technologies and Impact Studies of POPs
Bibliographic record
Abstract
Persistent organic pollutants (POPs) include pesticides which are organochlorine compounds, such as aldrin, chlordane, DDT, dieldrin, endrin, heptachlor, hexachlorobenzene, mirex, alpha and beta hexachlorocyclohexane (also by-products), chlordecone, and lindane. Industrial chemicals such as PCBs, hexachlorobenzene (also a pesticide), hexabromobiphenyl, hexa- and heptabromodiphenyl ether (commercial octabromodiphenyl ether), pentachlorobenzene, perfluorooctane sulfonic acid and its salts perfluorooctane sulfonyl fluoride, tetra- and pentabromodiphenyl ether (commercial pentabromodiphenyl ether), and unintentional by-product compounds, such as polychlorinated dioxins and furans (PCDD/Fs). The international community has repeatedly called for urgent global action to reduce and eventually eliminate the release of these chemicals. However, the emission characteristics, control technologies, atmosphere transport and toxicities of dioxins are not well investigated. This special issue in Aerosol and Air Quality Research features 24 papers submitted by researchers from Australia, Canada, China, Italy, Japan, Malaysia, Norway, Switzerland, Taiwan, Thailand, United Kingdom, USA and Vietnam. In these studies, the characteristics of POPs are investigated in air filters, tree bark, house dust, road dust, ambient air, fugitive nanoparticle, stack flue gas, fly ash, and serum samples.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.049 |
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".