The Distinct Composition and Transformation of Terrestrial Organic Carbon in the Yukon River Delta and Plume During the Mighty Spring Freshet
Bibliographic record
Abstract
Abstract Arctic amplification is leading to increased terrestrial organic carbon (terrOC) mobilization with downstream impacts on riverine and marine biogeochemistry. To improve quantification and characterization of terrOC discharged to the Arctic Ocean, Yukon River delta samples were collected during three stages of the annual hydrograph (ascending limb/peak freshet, descending limb, late summer) and across a land‐to‐ocean salinity gradient (0.08–29.06 ppt). All samples were analyzed for dissolved organic carbon (DOC) concentration and lignin phenols to determine seasonal variability in riverine terrOC and salinity‐induced transformation of highly aromatic terrestrial compounds. Additionally, the relationship between lignin and absorbance at 350 and 412 nm was assessed to determine the feasibility of using optical proxies for accurate quantification, both seasonally and across expansive salinity gradients. Lignin phenols were highest during the ascending limb/peak freshet (0.58–0.97 mg/100 mg OC) when riverine DOC was dominated by young vascular plant sources, whereas lignin phenols were lower (0.15–0.89 mg/100 mg OC) and riverine DOC more variable in terrestrial source and diagenetic state during the descending limb and late summer. Across the sampled salinity gradient, there was disproportionate depletion of lignin (up to 73%) compared to DOC (up to 22%). Finally, while optical proxies can be used to quantify lignin within seasonal or spatial contexts, increased uncertainty is likely when expanding linear correlations across Arctic land‐ocean continuums. Overall, results indicate seasonal, spatial, interannual, and climatic controls that are amplified during high‐flow conditions and important to constrain when investigating Arctic terrOC cycling and land‐ocean DOC flux.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".