?????? ?????? ?????? ??? PCDD/Fs, PCBs, PCNs??? ?????? ?????? ??? ????????? ??????
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs), and polychlorinated naphthalenes (PCNs) are three groups of structurally similar POPs. Among these POPs, 17 PCDD/Fs and 12 dioxin-like PCBs (dl-PCBs) have been investigated more widely due to their high toxicity to biota and humans. Several PCNs could have similar toxicity to PCDD/Fs and dl-PCBs. The Korean Ministry of Environment has conducted annually nationwide monitoring of them in soil\nsince 2008. In this study, soil samples at 61 national POPs monitoring stations in suburban, urban, and industrial areas were collected. After Soxhlet extraction and cleanup using multi-layer silica gel column, the pollutants were analyzed using GC/HRMS. The mean TEQ concentrations of PCDD/Fs, PCBs, and PCNs (3.25??4.40, 0.42??0.83, and 0.08??0.13 TEQ pg/g, respectively) in the industrial area were higher than those in the other areas. The sum of TEQ concentrations (PCDD/Fs+PCBs+PCNs) in some industrial stations (Banwol, Incheon, and Ulsan, Yeochun, and Pohang) were higher than those of the soil quality guideline from Canada (4 pg TEQ/g). Health risk assessments through ingestion, dermal contact, and inhalation intake were also performed using Korean data. As a result, cancer risks via the ingestion intake were higher than those from the others. The total cancer risks in the industrial area (mean: 3.34??10-7 for children and 3.07??10-7 for adults) were higher than those in other areas. However, cancer risks from the soil samples of South Korea were not exceeded the carcinogenic bench mark level described by US-EPA (1??10-6), suggesting the safe level.
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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 it