Microbially driven fate of terrigenous particulate organic matter in oceans
Bibliographic record
Abstract
Abstract A long‐standing enigma in oceanography is why terrestrial organic matter is “missing” in the global ocean, despite the considerable discharge into it every year. Although some explanations, such as mineralogical composition, hydrodynamic processes, and priming effect, have been proposed, we hypothesize that the essential mechanism behind the missing organic matter is microbial processing, for which the underlying coupled geochemical, molecular, and genetic evidence is unknown. An ultra‐large‐volume, long‐term river–seawater stratified simulation system was constructed to unravel the microbially driven fate of terrigenous particulate organic matter (POM) in oceans. Analysis of combining the molecular with POM chemical composition data suggests that Bacteroidetes could act as pioneers in the processing of terrigenous POM in oceans, degrading high‐molecular‐weight, high‐carbon compounds such as polysaccharides. Remaining low‐molecular‐weight nitrogenous organic matter is subsequently degraded by Planctomycetes and Proteobacteria. Isotopic signals show that this preferential degradation causes a distinct “aging” effect of POM, and along with nitrification enhanced by remineralization, causes a decrease in the POM C : N ratio. Degradation of terrigenous POM and bacterial biomass biosynthesis leads to positive deviations in δ15N and δ13C. Relatively refractory hydrocarbons, aromatic compounds, and phenols are accumulated by microbial processes in this system. This study provides mechanistic insights into the missing chemical and isotopic signals and microbially driven fate of terrigenous POM in the ocean, with important implications for how riverine material input affects marine carbon and nitrogen cycling.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".