The Role of Dissolved Organic Matter for Water Mass Characterization and Trace Metal Transport in the Arctic Ocean
Bibliographic record
Abstract
The Arctic Ocean is an ideal place for dissolved organic matter (DOM) research and studies of metal-organic interactions because it has limited exchange with the other oceans and has abundant sources of organics and trace metals in the upper water column like fluvial discharge and shelf input. In this dissertation, the value of DOM, and specifically the chromophoric and terrigenous portions of it (CDOM and tDOM, respectively), as natural tracers directly linked to the carbon cycle and giving insight on key processes like sea-ice freezing and thawing, halocline formation, and water masses circulation, are explored. Another purpose of this body of work is to improve understanding of the role of CDOM and tDOM for trace metals distribution, as trace element availability to phytoplankton plays a significant role in primary production.\nForemost, this dissertation research traces DOM from various sources in the Arctic Ocean by combining hydrographic characterization of water masses, water fraction analyses, and the optical and chemical characterization of DOM. The first part of this research examined the distributions of lignin phenols, CDOM, and optical properties in waters of the eastern Arctic, and and their relationship to dissolved iron (dFe) distributions to elucidate the sources, molecular characteristics and distributions of iron-binding ligands in the Arctic Ocean. The primary sources of iron-binding ligands appear to be the riverine discharge of terrigenous DOM, marine organic matter produced on the shelves, and degradation products of plankton-derived organic matter in the shelf sediments. The observed dFe distributions in the Arctic Ocean could not be explained by the presence of a single ligand type, but rather by a potpourri of ligand molecules of varying concentrations and binding strengths. In the second part, data from the International Arctic GEOTRACES project allowed us to expand the research into the western Arctic and examine the DOM distribution in the Chukchi sea shelf, Canada, Makarov, Amundsen and Nansen basins more closely. The Geotraces data set also allowed examining more dissolved trace metals in relation to DOM. Besides dFe, we were able to include manganese (dMn), nickel (dNi), copper (dCu), zinc (dZn), and cadmium (dCd). The DOM and trace metals correlations were investigated modus operandi to elucidate the sources, molecular characteristics and distributions of metal-binding ligands in the Arctic Ocean. In the last part, we compiled and merged some of the existing regional datasets of the in situ measurements of optical properties in an attempt to fill in the gaps in our knowledge of Arctic water mass circulation on a pan-Arctic scale. Based on absorbance and fluorescence measurements, we computed the widely-known indices including absorption coefficients a254, a350, spectral slopes S275–295, S350–400, S300–600, and fluorophores deciphered by the Parallel Factor Analysis (PARAFAC). These indices were proven to be helpful in tracing specific processes or chemical signatures in the Arctic Ocean on the regional level. We demonstrated that the optical properties of CDOM can be very beneficial on a pan-Arctic scale, e g., for localization and constraining the geographical extent of major oceanographic features like the Beaufort Gyre, the Transpolar drift and the halocline layers.
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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 | 0.001 | 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) | 0.000 | 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".