Distinct patterns of microbial metabolism associated to riverine dissolved organic carbon of different source and quality
Bibliographic record
Abstract
Abstract Dissolved organic carbon (DOC) in rivers contains a wide range of molecules that can be assimilated by microbes. However, there is today no integrated understanding of how the source and composition of this DOC regulate the extent to which the DOC can support microbial growth and respiration. We analyzed patterns in microbial metabolism of DOC from different streams and rivers in Québec, by combining short‐term bacterial production and respiration measurements with long‐term DOC loss and analyses of bacterial use of different single substrates. We show that distinct metabolic patterns indeed exist across catchments, reflecting the varying nature and sources of the DOC. For example, DOC from forest headwaters systematically supported the highest bacterial growth efficiency (BGE) that was recorded, while in contrast DOC in peat bog drainage was used with significantly lower BGE. The carbon consumption in clear mountain rivers, possibly represented by autochthonous algal DOC, supported the highest bacterial respiration rates and the highest long‐term DOC losses. By using principle component analysis, we demonstrate how the major axes of variation in all of the measured metabolic responses are tightly connected to spectrofluorometrical DOC composition indicators and to isotopic indicators of DOC source. If causality is assumed, our results imply that changes in DOC supply from different sources, for example, caused by land use or climate change, should result in dramatic changes in the patterns of aquatic microbial metabolism and thus in altered aquatic ecosystem functioning, with likely consequences for food‐web structures and greenhouse gas balances.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".