Quantitative estimates of labile and semi‐labile dissolved organic carbon in the western Arctic Ocean: A molecular approach
Bibliographic record
Abstract
A novel molecular approach based on carbon‐normalized yields of combined amino acids was developed to quantify concentrations of labile (L), semi‐labile (S), and refractory (R) dissolved organic carbon (DOC) in shelf and basin waters of the Western Arctic Ocean. Concentrations of L‐DOC were seasonally and spatially variable (0.1‐14.2 µmol L −1 ). In contrast, concentrations of S‐DOC were much less variable (20.2 ± 0.68 µmol L −1 SE). Average concentrations of L‐DOC in shelf waters increased from 0.7 µmol L −1 to 2.4 µmol L −1 between the spring and summer of 2002 and from 1.4 µmol L −1 to 3.9 µmol L −1 between the spring and summer of 2004. Primary productivity increased 2‐3‐fold between spring and summer, indicating a strong linkage between plankton and L‐DOC production. Patterns of L‐DOC abundance in surface waters are suggestive of multiple mechanisms of L‐DOC production, including direct release from phytoplankton and release during grazing. Concentrations of L‐DOC were not correlated with those of total DOC. Elevated concentrations of L‐DOC in halocline waters (40‐200‐m depth) of the Canada Basin indicated rapid transport of shelf‐produced DOC into the basin. Chemical and physical properties of basin waters with elevated L‐DOC concentrations indicated a sediment‐derived source of basin L‐DOC. The approach presented here for quantifying the labile and semilabile fractions of DOC is a potentially powerful tool for understanding processes controlling the distribution, production, and utilization of dissolved organic matter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".