The Role of Dissolved Organic Matter for Water Mass Characterization and Trace Metal Transport in the Arctic Ocean
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».