Composition of Stream Dissolved Organic Matter Across Canadian Forested Ecozones Varies in Three Dimensions Linked to Landscape and Climate
Bibliographic record
Abstract
Abstract Dissolved organic matter (DOM) is a key variable influencing aquatic ecosystem processes. The concentration and composition of DOM in streams depend on both the delivery of DOM from terrestrial sources and on aquatic DOM production and degradation. However, there is limited understanding of the variability of stream DOM composition at continental scales and the influence of landscape characteristics and disturbances on DOM across different regions. We assessed DOM composition in 52 streams at seven research sites across six forested ecozones in Canada in 2019–2022 using 26 indices derived from five analytical approaches: absorbance and fluorescence spectroscopy, liquid chromatography—organic carbon detection, Fourier‐transform ion cyclotron resonance mass spectrometry, and asymmetric flow field‐flow fractionation. Combined analyses showed clear clustering and redundancy across analytical techniques, and indicated that compositional variations were primarily related to three axes of DOM composition: (a) aromaticity, which was greater in low‐relief, wetland‐dominated catchments, (b) oxygenation, which was greater in colder and drier ecozones, and (c) biopolymer content, which was greater in lake‐influenced catchments. Variability in DOM composition among research sites was greater than variability of streams within a site and variability over time within a stream. Forest harvesting and wildfire disturbances had no common influence on DOM composition across research sites, emphasizing the need for regional studies. Our study provides a broad understanding of the variability of stream DOM composition and its associations with landscape and catchment characteristics at a subcontinental scale, and provides key insights for the choice and interpretation of DOM indices from various analytical approaches.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".