Organic Matter Redox State Driven by Specific Sources in Mangrove Sediments: A Case Study from Peruvian Ecosystems
Bibliographic record
Abstract
In order to determine the organic matter redox state in relation to specific sources in mangrove sediments, two 60 cm-long sediment cores were collected from mangrove-covered and mudflat zones within a mangrove forest in Peru. Sediment subsamples from these cores were analyzed to determine δ13C values and C:N ratios, whereas two redox indices, namely, electrochemical (fEAOM) and spectroscopical (A1650/A3400) indices, were taken from a previous study and correlated with the geochemical indices obtained from this work. These indices may provide accurate information on sedimentary organic matter diagenesis by oxidative processes through its redox state. The results show that the electrochemical index (fEAOM) and the spectroscopical index (A1650/A3400) for mangrove-covered sediments exhibited a positive correlation with δ13C values and a negative correlation with C:N molar ratios. These correlations suggest that the more labile sedimentary organic matter derived from non-terrestrial sources is in a more oxidized state than that derived from mangrove vegetation. However, this was not valid for mudflat zones, where non-significant correlations between geochemical indices were observed. Furthermore, the results suggest that the redox state of the organic matter deposited over time is dependent on source mixing influences, being better preserved in the presence of mangrove-derived organic matter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".