The chemical succession in anoxic lake waters as source of molecular diversity of organic matter
Bibliographic record
Abstract
The aquatic networks that connect soils with oceans receive each year 5.1 Pg of terrestrial carbon to transport, bury and process. Stagnant sections of aquatic networks often become anoxic. Mineral surfaces attract specific components of organic carbon, which are released under anoxic conditions to the pool of dissolved organic matter (DOM). The impact of the anoxic release on DOM molecular composition and reactivity in inland waters is unknown. Here, we report concurrent release of iron and DOM in anoxic bottom waters of northern lakes, removing DOM from the protection of iron oxides and remobilizing previously buried carbon to the water column. The deprotected DOM appears to be highly reactive, terrestrially derived and molecularly distinct, generating an ambient DOM pool that relieves energetic constraints that are often assumed to limit carbon turnover in anoxic waters. The Fe-to-C stoichiometry during anoxic mobilization differs from that after oxic precipitation, suggesting that up to 21% of buried OM escapes a lake-internal release-precipitation cycle, and can instead be exported downstream. Although anoxic habitats are transient and comprise relatively small volumes of water on the landscape scale, our results show that they may play a major role in structuring the reactivity and molecular composition of DOM transiting through aquatic networks and reaching the oceans.
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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 | 0.001 | 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) | 0.001 | 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".