Quantifying Earth’s Thermal History: Advances in Cosmogenic Noble Gas Paleothermometry, Diffusion Modeling, and Evaluation of Sea-Surface Temperature Reconstructions
Bibliographic record
Abstract
Understanding Earth’s past climate is essential for contextualizing modern climate change and refining models of future change. This dissertation advances approaches used to reconstruct terrestrial and oceanic paleotemperatures. The first study introduces the “MDD Tool Kit”, a novel optimization software for constraining multiple-diffusion domain (MDD) model parameters from stepwise degassing experiments. By simultaneously optimizing all model parameters without relying on user-defined activation energies, this tool improves the accuracy of thermal history reconstructions and reveals that previous methods may systematically underestimate paleo-temperatures. Application of the toolkit to 40Ar/39Ar thermochronology data from K-feldspar in Arizona yields thermal histories 50–75◦C warmer than prior estimates, yet consistent with independent thermochronometers. The second study applies cosmogenic noble gas paleothermometry to samples from Baffin Island, Canada. Utilizing the MDD Tool Kit, this work evaluates the reliability of using single-grain diffusion kinetics to represent whole-rock behavior and investigates the influence of laboratory storage conditions on helium diffusion. Results highlight that keeping samples cold after irradiation reduces the variation in reconstructed effective diffusion temperatures, emphasizing the need for careful sample storage protocols. Further, modeling experiments show that small variations in the fit of the MDD model can result in substantial differences in inferred paleotemperatures, underscoring the intrinsic non-uniqueness of the MDD model.In the final study, this dissertation explores sea surface temperature evolution off coastal California over the past 4.2 million years using clumped isotope and δ 18O paleothermometry and compares these findings with alkenone proxy records. While prior alkenone data suggest a marked cooling trend linked to coastal upwelling intensification, the clumped and stable-isotope data show little long-term change. Detailed scanning electron microscopy reveals pervasive early diagenetic alteration in the foraminifera samples, biasing the carbonate-based proxies and raising caution for future paleoclimate studies. The findings suggest that diagenetic alteration of planktonic foraminifera during burial can occur on the scale of centuries to millennia, far faster than previously believed.These studies advance paleothermometry by refining methods, improving data reliability, and revealing methodological uncertainties, ultimately contributing to more robust reconstructions of Earth’s climate history.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".