Terrestrial inputs and in-lake processes shape dissolved organic matter along a permafrost gradient
Bibliographic record
Abstract
Northern permafrost landscapes store vast pools of organic carbon that mobilize into lakes as dissolved organic matter (DOM).However, little is known about how nearshore terrestrial inputs interact with in-lake processes to shape DOM composition and fate.We compared shoreline porewater DOM with lake DOM across a 5 °C climate gradient spanning discontinuous to continuous permafrost in the Canadian Shield using ultra-high-resolution mass spectrometry and natural carbon isotopes (δ¹³C, Δ¹⁴C).We show that terrestrial porewater dominates the initial quantity and molecular makeup of DOM entering lakes.Yet, in-lake photodegradation, microbial processing, and modest algal production shift DOM toward less aromatic, more aliphatic molecules with more ¹⁴C-enriched radiocarbon signatures.Lake DOM-C concentrations averaged 38 % lower than porewater.The extent of these compositional shifts was best explained by the aquatic-to-terrestrial area ratio and associated surface-area-driven photoprocesses, whereas water residence time had weaker explanatory power.Because boreal and tundra lakes contain substantial terrestrial carbon, even modest changes in in-lake processing may amplify their sensitivity to permafrost thaw and landscape change.The aquatic-to-terrestrial area ratio provides a scalable, upstream-integrated metric for identifying lakes most likely to respond to shifts in terrestrial-aquatic coupling.Our study clarifies how shoreline inputs and basin configuration interact to shape DOM fate in permafrost lakes and offers a practical framework to anticipate changes in DOM export, greenhouse-gas release, and carbon burial as Arctic-boreal regions continue to warm.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".