Multi-proxy assessment of surface sediments using APPI-P FTICR-MS reveals a complex biogeochemical record along a salinity gradient in the Pearl River estuary and coastal South China Sea
Bibliographic record
Abstract
The Pearl River drains the second largest watershed in China, funnelling large amounts of freshwater and organic matter into the northern part of the South China Sea through an estuary characterized by pronounced biogeochemical gradients. In this study we analyzed organic extracts of surface sediments collected along land-sea transect that captures a transition from freshwater environment at the site of the Pearl River discharge, to marine settings at the most distal sampling point in the coastal South China Sea. Samples were analyzed using Fourier transform ion cyclotron mass spectrometry (FTICR-MS), to assess the molecular composition of the organic species present in the sediment and understand the sources and diagenesis of deposited organic matter. Results show a complex mixture of molecular markers, many of which can be used as proxies to distinguish between the freshwater and saline settings. For example, geochemical signal at the freshwater site is notably characterized by species belonging to hydrocarbon and sulphur-containing compound classes – these are likely markers of terrestrial, natural and/or anthropogenic organic matter inputs. On the other hand, samples from the coastal marine site bear a unique signature of putative tetrapyrrole species, molecular indicators of phytoplankton phaeopigments. Notably, some unusual and or novel species, such as sterenes and alkanones were putatively identified. These and other biomarkers species that can be detect using our single injection method provide convenient multiple proxies necessary for interpreting dynamic changes from land to the ocean, which have even been complicated by anthropogenic activities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".