OPTICAL CHARACTERIZATION, DISTRIBUTION AND SOURCES OF CHROMOPHORIC DISSOLVED ORGANIC MATERIAL(CDOM) IN THE CHANGJIANG RIVER ESTUARY IN JULY 2014
Bibliographic record
Abstract
Absorption and fluorescence spectroscopy, along with dissolved organic carbon(DOC) quantification, were employed to examine the sources and estuarine mixing behavior of chromophoric dissolved organic matter(CDOM) in the Changjiang River estuary in July 2014. CDOM abundance(as quantified by the absorption coefficient at 355 nm, a(355)), the absorption spectral slope over 275-295 nm(S275-295), and the specific UV absorbance at 254 nm(SUVA254) were all generally conservative across the freshwater-saltwater transitional zone. A localized elevation of CDOM occurred downstream in the mouth of the Huangpu River, revealing a subtle difference in the mixing pattern of CDOM between the North Port and the South Port, where as the constitutive property between the two ports was similar. DOC concentration([DOC]) can be predicted from the CDOM absorption coefficients at 275 nm(a(275)) and 295 nm(a(295)): ln[DOC] = 4.94–0.87ln[a(275)] + 0.90ln[a(295)], a(275)8.0 m–1; ln[DOC] = 4.77–6.79ln[a(275)] + 8.05ln[a(295)], a(275)≥8.0 m–1. The regression results demonstrate that CDOM absorbance could be used as the DOC tracer along the Changjiang River estuary. EEMs-PARAFAC(excitation-emission matrix fluorescence spectroscopy-parallel factor) analysis identified three humic-like components(C2, C4, and C5) and three protein-like components(C1, C3 and C6). The humic-like components possessed similar origins and correlated with a(355) and salinity. The protein-like components C1 and C6 were not significantly correlated to salinity and a(355), suggesting the protein-like components were closely related to the in situ microbial 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 | 0.001 | 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) | 0.000 | 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".