Unraveling the Biogeochemical Dynamics of Pyrite Formation and Trace Element Incorporation in Marine Sediments
Notice bibliographique
Résumé
Past studies in paleoenvironmental reconstruction have set out to bridge the gap that limits our understanding of the biogeochemical controls in the past oceans by developing redox proxies based on trace metal content, iron speciation, and pyrite formation. Many of those studies have relied on broad-scale temporal assumptions about ocean redox conditions inferred from idealized chemical processes and classifying past oceans into either oxygenated, ferruginous, or euxinic. The redox threshold values associated with these proxies can vary considerably among depositional systems and, for this reason, geochemical proxies should be scrutinized in multiple modern deposition systems of variable redox characteristics (stable and dynamic). This dissertation applies various geochemical tools to understand the biogeochemical controls on carbon, iron, and sulfur reaction rates during early diagenesis. Specifically, I test, refine, and expand the use of pyrite as a paleoredox proxy by expanding our understanding on the controls on pyrite formation and the incorporation of trace elements in pyrite during early diagenesis by investigating those relationships in modern marine depositional systems. First, I explore the early diagenetic processes occurring in marine sediments with emphasis on the carbon, iron, and sulfur cycle–the three main components in sedimentary pyrite formation. This effort is made by measuring nutrients, organic carbon, and iron and sulfur mineralogical characterization and coupling with reactive-transport diagenetic modelling to understand the diagenetic reactions that lead to iron-sulfide precipitation within the sedimentary profile. Two geographically distinct locations are studied in detail: (1) The Santa Monica Basin (SMB), an exceptionally iron dominated system, and (2) Saanich Inlet, BC, Canada, a fjord with high redox variability and transient euxinic bottom waters. Then, I explore the relationships between the chemical signatures in syngenetic and diagenetic framboidal pyrite and the bulk chemistry of the sediments and bottom waters from Saanich Inlet, to understand the mechanisms of sulfur fractionation and trace metal incorporation in pyrite during formation. \nIn Chapter 1, I explore the cryptic biogeochemical reactions that inhibit the formation of pyrite in the Santa Monica Basin. We find that this persistently hypoxic basin experiences limited bottom water O2 fluctuations that enables strong Fe redox cycling. This in turn enhances the formation of iron oxides bounded to organic matter (Fe[III]-OM complexes), limiting the reactivity of organic matter and iron oxides. The result is an extended ferruginous zone (dominated by iron oxides and dissolved Fe2+) and the suppression of a sulfidic zone in anoxic marine sediments. This study highlights key local controls on Fe availability in marginal basins and describes an intricate biogeochemical carbon-iron-sulfur cycling in modern and possibly ancient marine systems with important implications for Fe availability in the marine realm.\nChapter 2 investigates the influence of bottom water and sediment (early diagenetic) influence on framboidal pyrite trace element incorporation under a highly redox variable system in Saanich Inlet. The nonsteady-state diagenetic nature, defined by rapid sedimentation rate, sediment reworking, and transient euxinic conditions, produce a restricted diagenetic system evident from 34S enriched sulfur isotopic signatures in the sulfidic species (H2S, FeS, FeS2). I explore the viability for trace element content in syngenetic and early-diagenetic pyrite towards capturing the first-degree redox chemistry of the ocean. To determine if pyrite reveals biogeochemical properties obscured by bulk analyses, I compare in-situ trace metal content (LA-ICP-MS technique) from framboidal pyrite grains with bulk sediment and porewater trace metal content from Saanich Inlet. Following, Chapter 3 focuses on machine-learning approaches for classifying pyrite into formation types, based on in-situ sulfur isotopes and trace metal content, and the implications for pyrite as a biosignature. Finally, the final chapter is a collection of concluding remarks on implications of using pyrite as a proxy of past environmental conditions and as a possible biosignature for ancient life.\n
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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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| 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 ».