Unraveling the Biogeochemical Dynamics of Pyrite Formation and Trace Element Incorporation in Marine Sediments
Bibliographic record
Abstract
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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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 | 0.001 | 0.001 |
| 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".