A methodological study on the analysis of organic matter associated with iron oxides in marine sediments
Bibliographic record
Abstract
Coastal shelves significantly contribute to the burial of natural organic matter (NOM) in marine sediments, with about 21.5 ± 8.6% of NOM associated with reactive iron oxides, which preserve NOM from biodegradation. Quantifying this preserved NOM requires a method to release bound carbonaceous molecules from iron minerals. The citrate-bicarbonate-dithionate (CBD) method is commonly used to liberate NOM from iron oxides through reductive dissolution. This method includes a control experiment using an NaCl solution to distinguish NOM associated with iron oxides from that leaching out from other minerals. This study aims to determine if desorbed NOM during the control experiment comes from other minerals or is loosely bound to iron oxides. We synthesized lepidocrocite (γ-FeOOH) in the presence and absence of plankton-extracted NOM to mimic sorption and conducted similar experiments on kaolinite, montmorillonite, and their mixture, representing common clay minerals in sediments. Quantifying the carbon content revealed that NOM associated with γ-FeOOH is 1.5–9.0 times greater than with other minerals. Post-treatment results indicate a 22.2%–42.7% loss of NOM associated with iron oxides, suggesting that deducting NOM lost during the control step underestimates the amount of carbon preserved by iron minerals in marine sediments. • The efficiency of the method to quantify Fe-associated organic matter is revisited. • Organic matter released in the control experiment is partly associated with iron. • Clay minerals also release organic matter during the control experiment. • As Usually applied, the method underestimates Fe associated organic matter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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 teacher head, 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".