A Late Miocene to Late Pleistocene Reconstruction of Precipitation Isotopes and Climate From Hydrated Volcanic Glass Shards and Biomarkers in Central Alaska and Yukon
Bibliographic record
Abstract
Abstract The Pliocene (5.3–2.6 Ma), an epoch with periods of climatic warmth and possible analogue for the future, has been well‐characterized globally by marine geochemical proxies. However, far less is known about Pliocene warmth at continental high latitudes, where the greatest impacts of warming are expected. This study seeks to better characterize the Pliocene climate of central Alaska and Yukon based on a reconstruction of the stable hydrogen isotope composition of precipitation relative to modern (ΔδDprecip) preserved in volcanic glass shards, a proxy for mean air temperature. The studied tephras are from a regional suite of outcrops that, when assembled into a composite record of ΔδDprecip, can be used to resolve broad trends during the late Miocene (6.7–5.86 Ma, n = 5), Pliocene (5.08–2.81 Ma, n = 7), and late Pleistocene (0.74–0.03 Ma, n = 3). These trends indicate that Pliocene ΔδDprecip estimates were generally more enriched in heavy isotopes than the latest Miocene, Pleistocene, and modern intervals. ΔδDprecip is likely influenced by changes in regional boundary conditions including orographic barriers, depositional environments, and ocean‐atmospheric circulation, but ΔδDprecip trends are most consistent with reconstructed temperatures from Yukon‐Alaska and North Pacific marine records. As such, this record appears predominantly sensitive to regional climate. Furthermore, qualitative temperature inferences from branched‐chain glycerol dialkyl glycerol tetraethers (brGDGTs) from four of our sites dating between 2.91 and 6.17 Ma corroborate elevated temperatures during the early Pliocene. Overall, this study demonstrates the viability of volcanic glass δD as a proxy for ΔδDprecip and late Cenozoic climate change in this region.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".