Bibliographic record
Abstract
To investigate the distribution characteristics of grain size and organic matter of surface sediments from the Nakdong-Goryeong Mid-watershed, surface sediments were collected and analyzed. The samples were collected from six sited at four different times between May 2013 and May 2014. The were analyzed for grain size, water content, ignition loss, chemical oxygen demand, total organic carbon and total nitrogen. The surface sediments were mainly composed of medium sand (mean 44.7%) and coarse sand (mean 32.8%) and became coarser in May 2014. Fine sediments at the site NG-2 were poorly sorted and positively skewed, and occur in a tributary environment that is relatively low-energy compared with the other sites. The water content at the studied sites (15.3 ~ 34.9%) averaged 20.25%, and ignition loss (0.4 ~ 5.8%) and total nitrogen (274 ~ 2493 mg/kg) averaged 1.33% and, 696 mg/kg, respectively. These values indicated that the sediments were not seriously contaminated when compared with the sediment pollution evaluation standard of the National Institute of Environmental Research. The chemical oxygen demand (mean 0.17%) was at the non-polluted level compared with United States Environmental Protection Agency sediment quality standards. The total organic carbon (mean 0.18%) at all sites except site NG-2 (lowest effect level) was the no effect level of the Ontario sediment quality guidelines. The COD/IL (0.02 ~ 0.20) and C/N (0.73 ~ 6.76) were less than 1 and 10, respectively. Organic matter in the study area produced naturally from aquatic organisms. Results of principal component analysis showed that fine sediments (very fine sand and silt) were significantly affected by organic matters (ignition loss, chemical oxygen demand, total organic carbon and total nitrogen). In addition, the highest organic matters content in the study area occurred at the site with the finest sediments (NG-2).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".