Universal microbial reworking of dissolved organic matter along soil gradients
Bibliographic record
Abstract
Soils lose a large amount of carbon annually to freshwaters as dissolved organic matter (DOM), which, if degraded, can undermine climate change mitigation. The degradation state of DOM in aquatic ecosystems can reflect the distance from its source, with DOM increasingly dominated by similar compounds as degradation proceeds. However, the processes underlying the degradation of DOM and its generality across environments are poorly understood. Here we found DOM changed similarly along two soil-aquatic gradients irrespective of environmental conditions. We tracked DOM across soil depths and hillslope positions in forest headwater catchments using ultra-high-resolution mass spectrometry and related its composition to soil microbiomes and physical chemistry. Along both gradients, carbohydrate-like and unsaturated hydrocarbon-like compounds increased in mass, suggestive of microbial reworking of plant material. Most of the variation in the abundance of these compounds (>56%) was related to the expression of genes important for breaking down plant-derived carbohydrates. Our results highlight the value of high-resolution molecular data in understanding global carbon cycles, directly implicate microbial processing in shifting DOM towards universal compounds in soils, and suggest that this process is generalizable across ecosystems and spatiotemporal scales. This consistent degradation process could provide insights for estimating the state of DOM in different environments and inform the management of soil-to-stream carbon losses.
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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) | 0.001 | 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".