The river as a chemostat: fresh perspectives on dissolved organic matter flowing down the river continuum
Bibliographic record
Abstract
A better understanding is needed of how hydrological and biogeochemical processes control dissolved organic carbon (DOC) concentrations and dissolved organic matter (DOM) composition from headwaters downstream to large rivers. We examined a large DOM dataset from the National Water Information System of the US Geological Survey, which represents approximately 100 000 measurements of DOC concentration and DOM composition at many sites along rivers across the United States. Application of quantile regression revealed a tendency towards downstream spatial and temporal homogenization of DOC concentrations and a shift from dominance of aromatic DOM in headwaters to more aliphatic DOM downstream. The DOC concentration–discharge (C-Q) relationships at each site revealed a downstream tendency towards a slope of zero. We propose that despite complexities in river networks that have driven many revisions to the River Continuum Concept, rivers show a tendency towards chemostasis (C-Q slope of zero) because of a downstream shift from a dominance of hydrologic drivers that connect terrestrial DOM sources to streams in the headwaters towards a dominance of instream and near-stream biogeochemical processes that result in preferential losses of aromatic DOM and preferential gains of aliphatic DOM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".