The impacts of chemical composition on bacterial processing of dissolved organic matter in the deep Arctic Ocean
Bibliographic record
Abstract
The cycling of dissolved organic matter (DOM) in the ocean plays a central role in carbon dynamics, influencing the ocean's ability to sequester carbon and regulate climate. However, the microbial processes that govern the transformation and persistence of DOM across different water masses remain poorly understood. In this study, we explore how a single bacterial community processes DOM from four distinct water masses ( i.e , Pacific winter water, Atlantic halocline, Atlantic water - Barents Sea Strait branch, and deep temperature minimum) in the Canada Basin of the Arctic Ocean, using a combination of optical and molecular techniques to reveal the microbial mechanisms behind DOM reactivity and persistence. Although all water masses contained the same fluorescent components, each underwent unique processing by the microbial community after 20 days of laboratory-controlled incubation. This suggests that the composition of specific fluorescent components likely varies between different water layers, with an increase in these features by the end of the incubation. The number of common molecular features measured with a trapped ion mobility spectrometer coupled with a time-of-flight mass spectrometer (TIMS-TOF) and an electrospray ionization source (positive ionization mode) was higher in contiguous water masses, likely due to water mass mixing resulting in the formation of the Atlantic halocline. Spearman's rank correlations between fluorescence characteristics and TIMS-TOF features established connections between fluorescent characteristics and molecular formulas. Twenty-two distinct molecular features were related to the four humic-like. None of them were associated with tyrosine-like, revealing the distinct molecular-optical linkages between humic- and protein-like DOM. Overall, our findings indicate that the DOM reactivity depends on its optical and molecular composition. The mechanistic understanding of the factors that control the persistence of DOM in the ocean is essential for predicting the ocean's role in the global carbon cycle.
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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 | 0.000 |
| 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".