Insights into Water Mass Circulation and Origins in the Central Arctic Ocean from in-situ Dissolved Organic Matter Fluorescence.
Bibliographic record
Abstract
The Arctic Ocean receives a large loading of dissolved organic matter (DOM) from its catchment and shelf sediments, which can be traced across much of the basin. This signature can be used as a tracer of water mass circulation. On the shelf seas, the combination of freshwater loading from rivers and ice formation modify water mass densities and mixing considerably. These waters are the source of the halocline layer that covers much of the Arctic ocean. Our knowledge of the origins, formation and maintenance of the halocline has mostly arisen from CTD profiles and chemical tracers such as oxygen stable isotopes and inorganic nutrients, but the halocline also contains elevated levels of DOM (DOM). Here we demonstrate how this can be used as a tracer and help improve our understanding of ocean circulation. DOM fluoresce can be measured using in-situ fluorometers and mounted on autonomous platforms these can provide high spatial resolution measurements. Here we present data derived from several Ice Tethered Profilers. The data offer a unique spatial coverage of the distribution of DOM in the surface 800m below Arctic ice. Water mass analysis using temperature, salinity and DOM fluorescence, clearly distinguishs the halocline contribution of Siberian terrestrial DOM and marine DOM from the Chuckchi shelf. The findings offer a new approach to trace the distribution of Pacific waters and its export from the Arctic Ocean. Our results indicate the potential to extend the approach to fraction freshwater contributions from, sea ice melt, riverine discharge and Pacific water.
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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 | 0.001 | 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".