Biodegradation of Terrigenous Organic Matter in a Stratified Large-Volume Water Column: Implications of the Removal of Terrigenous Organic Matter in the Coastal Ocean
Bibliographic record
Abstract
Large amounts of terrigenous organic matter (TOM) are delivered to the ocean every year. However, removal processes of TOM in the ocean are still poorly constrained. Here, we report results from a 339-day dark incubation experiment with a unique system holding a vertically stratified freshwater-seawater column. The quality and quantity of dissolved organic matter (DOM), RNA-based size-fraction microbial communities, and environmental factors were high-frequency-monitored. Microbial processes impacted TOM composition, including an increased DOM photobleaching rate with incubation time. The mixed layer had changed the bacterial community structure, diversity, and higher oxygen consumption rate. A two-end member modeling analysis suggested that estimated nutrient concentrations and prokaryotic abundance were lower, and total dissolved organic carbon was higher than that of the measured values. These results imply that DOM biodegradation was stimulated during freshwater-seawater mixing. In the bottom layer, fluorescent DOM components increased with the incubation time and were significantly positively related to highly unsaturated, oxygenated, and presumably aromatic compound molecular formulas. These results suggest that surfaced-derived TOM sinking leads to increased DOM transformation and likely results in carbon storage in the bottom water. Overall, these results suggest that microbial transforming TOM plays more important biogeochemical roles in estuaries and coastal oceans than what we know before.
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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 | 0.000 | 0.000 |
| 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".