Anthropogenic Activities Have Contributed Moderately to Increased Inputs of Organic Materials in Marginal Seas off China
Bibliographic record
Abstract
Sediment has been recognized as a gigantic sink of organic materials and therefore can record temporal input trends. To examine the impact of anthropogenic activities on the marginal seas off China, sediment cores were collected from the Yellow Sea, the inner shelf of the East China Sea (ECS), and the South China Sea (SCS) to investigate the sources and spatial and temporal variations of organic materials, i.e., total organic carbon (TOC) and aliphatic hydrocarbons. The concentration ranges of TOC were 0.5-1.29, 0.63-0.83, and 0.33-0.85%, while those of Σn-C14-35 (sum of n-alkanes with carbon numbers of 14-35) were 0.08-1.5, 0.13-1.97, and 0.35-0.96 μg/g dry weight in sediment cores from the Yellow Sea, ECS inner shelf, and the SCS, respectively. Terrestrial higher plants were an important source of aliphatic hydrocarbons in marine sediments off China. The spatial distribution of Σn-C14-35 concentrations and source diagnostic ratios suggested a greater load of terrestrial organic materials in the Yellow Sea than in the ECS and SCS. Temporally, TOC and Σn-C14-35 concentrations increased with time and peaked at either the surface or immediate subsurface layers. This increase was probably reflective of elevated inputs of organic materials to marginal seas off China in recent years, and attributed partly to the impacts of intensified anthropogenic activities in mainland China. Source diagnostics also suggested that aliphatic hydrocarbons were mainly derived from biogenic sources, with a minority in surface sediment layers from petroleum sources, consistent with the above-mentioned postulation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".