How humans alter dissolved organic matter composition in freshwater: relevance for the Earth’s biogeochemistry
Bibliographic record
Abstract
Abstract Dissolved organic matter (DOM) is recognized for its importance in freshwater ecosystems, but historical reliance on DOM quantity rather than indicators of DOM composition has led to an incomplete understanding of DOM and an underestimation of its role and importance in biogeochemical processes. A single sample of DOM can be composed of tens of thousands of distinct molecules. Each of these unique DOM molecules has their own chemical properties and reactivity or role in the environment. Human activities can modify DOM composition and recent research has uncovered distinct DOM pools laced with human markers and footprints. Here we review how land use change, climate change, nutrient pollution, browning, wildfires, and dams can change DOM composition which in turn will affect internal processing of freshwater DOM. We then describe how human-modified DOM can affect biogeochemical processes. Drought, wildfires, cultivated land use, eutrophication, climate change driven permafrost thaw, and other human stressors can shift the composition of DOM in freshwater ecosystems increasing the relative contribution of microbial-like and aliphatic components. In contrast, increases in precipitation may shift DOM towards more relatively humic-rich, allochthonous forms of DOM. These shifts in DOM pools will likely have highly contrasting effects on carbon outgassing and burial, nutrient cycles, ecosystem metabolism, metal toxicity, and the treatments needed to produce clean drinking water. A deeper understanding of the links between the chemical properties of DOM and biogeochemical dynamics can help to address important future environmental issues, such as the transfer of organic contaminants through food webs, alterations to nitrogen cycling, impacts on drinking water quality, and biogeochemical effects of global climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".