Abrupt shifts in the concentration, composition, and reactivity of dissolved organic carbon from terrestrial to aquatic compartments across boreal watersheds
Bibliographic record
Abstract
Dissolved organic carbon (DOC) plays a critical role in the boreal aquatic carbon cycle, serving as a vital link between land and water ecosystems. Despite rapidly increasing knowledge of the carbon cycling in the terrestrial and aquatic ends of watersheds, understanding of the biogeochemical processing of DOC pools along land-water continua remains poor. We determined the concentration, composition, and reactivity of DOC in key watershed compartments (forest soil water, riparian zones (RZ) soil water, streams, and lakes) of 16 watersheds in two regions of boreal Quebec. Our findings show a substantial decline (3.3 times) in DOC concentration across the land-water interface from soils to lakes. We also found higher spatial variability in DOC concentration in the terrestrial compared to the aquatic compartments of watersheds. Despite these variations, the percentage of bio- and photo-degradable DOC remained consistent along the land-water continuum. We further found high concentrations of protein-like DOM in the forest soil water and RZ samples, yet these concentrations diminished significantly in the aquatic environment, consistent with efficient loss of this DOM in standardized experiments. These findings suggest a terrestrial source of highly bio-reactive protein-like components and a rapid loss of terrestrial DOC as it leaves land and is drained into streams and lakes.
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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 | 0.001 | 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) | 0.001 | 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".