Influence of Microbial Activities on Fluorescent Dissolved Organic Matter in the Dark Canada Basin Waters
Bibliographic record
Abstract
Abstract The fluorescence characteristics of dissolved organic matter (DOM) were measured to determine the distribution and drivers of DOM in the dark Canada Basin waters. We studied the relationship between fluorescent DOM (FDOM) and apparent oxygen utilization (AOU) in the whole water column as well as in the main water masses (Pacific Winter Water, Atlantic halocline (AH), Atlantic water—Fram Strait and Barents Sea branches, Deep Temperature Minimum, and Canada Basin deep water). The relative water mass fractions of different water masses were estimated using a five end‐members mixing model. The distribution of water mass fractions was found to be in good agreement with the distribution based on the traditional temperature‐salinity diagram. The fractions of AH waters were linked with AOU, highlighting the significant role played by mineralization in shaping AH humic‐like levels. The slope of the relationship between in situ FDOM—AOU was greater than those reported in the other oceans worldwide. This suggests that the AH is a hot spot for microbial mineralization of humic‐like DOM. On the other hand, the relationships with the humic‐like intensities differed among the four humic‐like components in the >125 m waters, indicating distinct environmental dynamics and biogeochemical roles for each humic‐like component.
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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".