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
In order to assess the level of contamination and identify the priority contaminants in the Busan coast, intensive sediment sampling was conducted and persistent organic pollutants and heavy metals were analyzed. The Sediment Quality Index (SQI) was derived based on the contaminant data by comparing with Sediment Quality Guidelines (SQGs) established in Korea, Canada, and Australia/New Zealand. Toxic contaminants were found to be widely distributed across the coast. Among organic contaminants, PAHs showed the highest concentration, followed by butyltins, nonylphenols, PBDEs, DDTs, PCBs, HCHs and CHLs. Heavy metals were also abundantly detected with the highest concentration of Zn followed by Cu > Cr > Pb > Ni > As > Cd > Hg. Compared to organic contaminants, most heavy metals, except for Cu and Hg, were homogeneously distributed along the coast in a good relationship with total organic carbon of sediment particles. In general, the concentrations of organic compounds and heavy metals were highest at the inner part of harbor areas with a tendency to decline from inside areas to the outside, indicating the high loading of pollutants from harbors. A high exceedance for low-SQGs and high-SQGs was found for TBT, p,p’-DDT, p,p’-DDD, Cu and Zn. The SQI scores calculated from low-SQGs and high-SQGs were in the range of 18−100 and 54−100, respectively. The inner part of Busan Harbor, Dadaepo Harbor, and Gamcheon Harbor were observed as being regions of concern. Overall, TBT, Cu, and p,p’-DDT were the chemicals most frequently exceeding SQGs and influencing SQI scores.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it