High Voltage: The Molecular Properties of Redox-Active Dissolved Organic Matter in Northern High-Latitude Lakes
Bibliographic record
Abstract
Redox-active functional groups in dissolved organic matter (DOM) are crucial for microbial electron transfer and methane emissions. However, the extent of aquatic DOM redox properties across northern high-latitude lakes and their relationships with DOM composition have not been thoroughly described. We quantified electron donating capacity (EDC) and electron accepting capacity (EAC) in lake DOM from Canada to Alaska and assessed their relationships with parameters from absorbance, fluorescence, and ultrahigh resolution mass spectrometry (FT-ICR MS) analyses. EDC and EAC are strongly tied to aromaticity and negatively related to aliphaticity and protein-like content. Redox-active formulae spanned a range of aromaticity, including highly unsaturated phenolic formulae, and correlated negatively with many aliphatic N and S-containing formulae. This distribution illustrates the compositional diversity of redox-sensitive functional groups and their sensitivity to ecosystem properties such as local hydrology and residence time. Finally, we developed a reducing index ( RI ) to predict EDC in aquatic DOM from FT-ICR MS spectra and assessed its robustness using riverine DOM. As the hydrology of the northern high-latitudes continues to change, we expect differences in the quantity and partitioning of EDC and EAC within these lakes, which have implications for local water quality and methane emissions.
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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 | 0.001 |
| 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) | 0.000 | 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".