Universal microbial reworking of dissolved organic matter along environmental gradients
Bibliographic record
Abstract
Soils are losing increasing amounts of carbon annually to freshwaters as dissolved organic matter (DOM), which, if degraded, can offset their carbon sink capacity. However, the processes underlying DOM degradation across environments are poorly understood. Here we show DOM changes similarly along soil-aquatic gradients irrespective of environmental differences. Using ultrahigh-resolution mass spectrometry, we track DOM along soil depths and hillslope positions in forest catchments and relate its composition to soil microbiomes and physico-chemical conditions. Along depths and hillslopes, we find carbohydrate-like and unsaturated hydrocarbon-like compounds increase in abundance-weighted mass, and the expression of genes essential for degrading plant-derived carbohydrates explains >50% of the variation in abundance of these compounds. These results suggest that microbes transform plant-derived compounds, leaving DOM to become increasingly dominated by the same (i.e., universal), difficult-to-degrade compounds as degradation proceeds. By synthesising data from the land-to-ocean continuum, we suggest these processes generalise across ecosystems and spatiotemporal scales. Such general degradation patterns can help predict DOM composition and reactivity along environmental gradients to inform 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) | 0.000 | 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".