Terrigenous dissolved organic matter in the Arctic Ocean and its transport to surface and deep waters of the North Atlantic
Bibliographic record
Abstract
Surface waters of the Arctic Ocean have the highest concentrations of dissolved organic carbon (DOC) and terrigenous dissolved organic matter (DOM) of all ocean basins. Concentrations of dissolved lignin phenols in polar surface waters are 7‐fold to 16‐fold higher than those in the Atlantic and Pacific oceans, and stable carbon isotopic compositions of DOM are depleted in 13C by 1–2‰ relative to those in the Atlantic and Pacific. The large contribution of terrigenous DOM from Arctic rivers is responsible for the elevated concentrations of DOC in polar surface waters. The distribution of terrigenous DOM in polar surface waters is very heterogeneous, but on average we estimate 14–24% of the DOC is of terrestrial origin. Stable nitrogen isotopic compositions were useful for distinguishing DOM of Pacific and Atlantic origins as well as terrigenous and marine origins. The size distribution and composition of lignin phenols provide some evidence of photochemical transformations of terrigenous DOM, but it appears this process is not extensive in polar surface waters. The extent to which terrigenous DOM is removed from the Arctic Ocean by microbial degradation is less clear and warrants further study. Physical transport of terrigenous DOC to the North Atlantic is a major mechanism for its removal from the Arctic. The East Greenland Current alone exports 4.4–6.6 Tg of terrigenous DOC annually to the North Atlantic. Terrigenous DOC of Arctic origin was identified for the first time in components of North Atlantic Deep Water. Preliminary estimates indicate that ∼1 Tg of terrigenous DOC is exported from the Arctic in Denmark Strait Overflow Water with an additional ∼0.7 Tg in Classical Labrador Sea Water. Together, these exports compose approximately 25–33% of the terrigenous DOC discharged annually to the Arctic via rivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".