Hydrologic connectivity determines dissolved organic matter biogeochemistry in northern high‐latitude lakes
Bibliographic record
Abstract
Abstract Northern high‐latitude lakes are undergoing climate‐induced changes including shifts in their hydrologic connectivity with terrestrial ecosystems. How this will impact dissolved organic matter (DOM) biogeochemistry remains uncertain. We examined the drivers of DOM composition for lakes in the Yukon Flats Basin in Alaska, an arid region of low relief that is characteristic of over one‐quarter of circumpolar lake area. Utilizing the vascular plant biomarker lignin, chromophoric dissolved organic matter (CDOM), and ultrahigh‐resolution mass spectrometry, we interpreted DOM compositional changes using lake‐water stable isotope (δ18O‐H2O) composition as a proxy for lake hydrologic connectivity with the landscape. We observed a relative decrease in CDOM in more hydrologically isolated lakes (enriched δ18O‐H2O) without a corresponding decrease in dissolved organic carbon (DOC) concentration. Although DOC and CDOM were weakly correlated, a significant positive relationship between lignin and CDOM (r2 = 0.67) demonstrates that optical parameters are useful for estimating lignin concentration and thus vascular plant contribution to lake DOM. Indicators of allochthonous DOM, including lignin carbon normalized yields, CDOM aromaticity proxies, and relative abundances of polyphenolic and condensed aromatic compound classes, were negatively correlated with δ18O‐H2O (r2 > 0.45), suggesting there is little allochthonous DOM supplied to many of these hydrologically isolated lakes. We conclude that decreased lake hydrologic connectivity, driven by ongoing climate change (i.e., decreased precipitation, warming temperatures), will reduce allochthonous DOM contributions and shift lakes toward lower CDOM systems with ecosystem‐scale ramifications for heat transfer, photochemical reactions, productivity, and ultimately their biogeochemical function.
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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 | 0.000 | 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) | 0.001 | 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".