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Record W7015056756

The Role of Dissolved Organic Matter for Water Mass Characterization and Trace Metal Transport in the Arctic Ocean

2022· dissertation· en· W7015056756 on OpenAlexaboutno aff

Bibliographic record

VenueOakTrust (Texas A&M University Libraries) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsTerrigenous sedimentDissolved organic carbonArcticColored dissolved organic matterPhytoplanktonTrace metalGeotracesWater columnWater massSeawater
DOInot available

Abstract

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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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.149
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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