Distribution, seasonality, optical characteristics, and fluxes of dissolved organic matter (DOM) in the Pearl River (Zhujiang) estuary, China
Bibliographic record
Abstract
Abstract. Dissolved organic carbon concentration in the Pearl River estuary (PRE) of China was measured in May, August, and October 2015 and January 2016. Chromophoric and fluorescent dissolved organic matter (CDOM and FDOM) in the latter three seasons were characterized by absorption and fluorescence spectroscopy. Parallel factor analysis of the fluorescence spectra identified two protein-like, two humic-like, and one oxidized quinone-like FDOM components. The seasonality of average DOM abundance varied as follows: DOC: May (156 μmol L−1) > January (114 μmol L−1) ≈ August (112 μmol L−1) > November (86 μmol L−1); CDOM absorption at 330 nm: August (1.76 m−1) > November (1.39 m−1) ≈ January (1.30 m−1); FDOM expressed as the sum of the maximum fluorescence intensities of all FDOM components: November (1.77 R.U.) > August (1.54 R.U.) ≈ January (1.49 R.U.). Average DOM abundance in surface water was higher than in bottom water, their difference being marginal (0.1–10 %) for DOC in all seasons and for CDOM and FDOM in November and January, and moderate (16–21 %) for CDOM and FDOM in August. DOC showed little cross-estuary variations in all seasons while CDOM and FDOM in January were higher on the west side of the estuary than in the middle and on the east side. All three variables exhibited large variations and/or rapid drawdowns at the head of the estuary (salinity
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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 | 0.001 | 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".