Application of multiple geochemical markers to investigate organic pollution in a dynamic coastal zone
Bibliographic record
Abstract
Multiple geochemical markers, including aliphatic hydrocarbons (n-alkanes), linear alkylbenzenes (LABs), and polycyclic aromatic hydrocarbons (PAHs), were employed to relate sediment organic chemical pollution in the coastal zone off South China to socioeconomic development there. Concentrations of Σn-C(15-35) (n-alkanes with 15-35 carbon atoms), ΣLAB (sum of C(10) to C(13) LABs), and Σ(26) PAH (sum of 26 PAH compounds) ranged from 110 to 3,160, 11 to 160, and 26 to 600 ng/g, with medians of 730, 40, and 230 ng/g, respectively. Natural hydrocarbons were mainly derived from terrestrial higher plant waxes, and in minor amounts from aquatic plankton and bacteria. Compositions of LABs indicated that considerable amounts of poorly treated wastewater had been directly discharged or transported to the eastern and western coastal areas of Guangdong Province. In addition, anthropogenic hydrocarbons were derived largely from vehicular emissions and combustion of domestic coal and biomass and to a lesser extent from oil spills. Eastern and western coastal sediments contained higher levels of LABs but lower levels of PAHs than those of the Pearl River Estuary, a coastal area of the Pearl River Delta. This spatial pattern of organic pollution was consistent with chemical use patterns. The eastern and western regions of Guangdong Province are economically less developed than the Pearl River Delta region, where more domestic wastewater treatment plants have been built. However, greater amounts of energy are consumed in the latter region to produce more combustion-derived PAH contamination.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".