Unraveling the role of land use and microbial activity in shaping dissolved organic matter characteristics in stream ecosystems
Bibliographic record
Abstract
Surface water samples were collected from 43 streams distributed throughout watersheds of mixed land use in southern Ontario, Canada. Absorbance and fluorescence spectroscopy with parallel factor analysis (PARAFAC) was used to characterize dissolved organic matter (DOM). DOM characteristics were related to environmental variables, microbial activity indicators (bacterial production and extracellular leucine aminopeptidase activity), and riparian land use to understand better how these factors influence DOM in streams. PARAFAC produced a six‐component model (C1 to C6). Temperature correlated with each PARAFAC component, suggesting that water source, drainage area, and light penetration broadly affected DOM characteristics. C1 and C2 represented terrestrial, humic‐like DOM fluorophore groups and comprised 41–65% of stream DOM fluorescence. C5, a tryptophan‐like component, related negatively to a humification index but positively to leucine‐aminopeptidase activity and recently produced DOM, suggesting that C5 consisted of autochthonous, microbially produced DOM. C3, C4, and C6 showed signs of quinone‐like, humic‐like, and microbial transformable fluorophores. The distribution of these potentially redox‐active PARAFAC components indicated that DOM was in a more reduced state in streams with higher bacterial production and agricultural land use than in streams with increased wetlands area, which had greater relative abundance of the oxidized quinone‐like component. Anthropogenic land use and microbial activity altered the quantity and quality of DOM exported from human‐affected streams from that observed in forest‐ and wetland‐dominated streams. DOM in agriculturally affected streams was likely more labile and accessible to the microbial community than DOM in wetland streams, which supported low rates of microbial activity.
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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 | 0.001 |
| 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) | 0.000 | 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".