Characteristics of Dissolved Organic Carbon in Boreal Lakes: High Spatial and Inter‐Annual Variability Controlled by Landscape Attributes and Wet‐Dry Periods
Bibliographic record
Abstract
Abstract Concentration and chemical composition of dissolved organic carbon (DOC) influence several lake functions; greenhouse gas exchange, nutrient cycling, food webs, and water treatability. To assess spatial and inter‐annual controls on DOC characteristics, 34 lakes were sampled annually for 8 years on the Boreal Plains, Western Canada—a region with heterogeneous surficial geology, and a sub‐humid climate with pronounced inter‐annual wet‐dry periods. Large spatial variability in long‐term average DOC concentration (10–49 mg C L−1) and aromaticity (SUVA254: 1.2–3.9 L mg−1 C m−1) among lakes was found. Higher DOC concentrations and aromaticity were associated with lakes in watersheds with fine‐textured surficial geology and with relatively large contributions through shallow, organic‐rich flow paths. Lake DOC aromaticity was also higher in lakes with lower evaporative enrichment, regardless of surficial geology, indicating shorter lake water residence times and less within‐lake degradation of allochthonous DOC. High inter‐annual variability for both DOC and aromaticity was observed, with coefficients of variation at 10.9 ± 4.6% and 11.1 ± 2.5% among lakes, respectively. Inter‐annual variability in DOC concentrations had low synchronicity among lakes, with patterns of variability linked to surficial geology and primarily responsive to short‐term cumulative precipitation. Conversely, inter‐annual variability in aromaticity had high synchronicity among lakes, driven by longer‐term cumulative precipitation and shifts in lake water residence times. Our study shows it is necessary to consider lake hydrogeomorphic setting and climate to understand spatial and inter‐annual variability in lake DOC characteristics and associated lake functions, and that Boreal Plains lakes have high climate sensitivity.
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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 | 0.000 |
| 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) | 0.000 | 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".