Late‐Pleistocene precipitation δ<sup>18</sup>O interpolated across the global landmass
Bibliographic record
Abstract
Abstract Global water cycles, ecosystem assemblages, and weathering rates were impacted by the ∼4°C of global warming that took place over the course of the last glacial termination. Fossil groundwaters can be useful indicators of late‐Pleistocene precipitation isotope compositions, which, in turn, can help to test hypotheses about the drivers and impacts of glacial‐interglacial climate changes. Here, a global catalog of 126 fossil groundwater records is used to interpolate late‐Pleistocene precipitation δ18O across the global landmass. The interpolated data show that extratropical late‐Pleistocene terrestrial precipitation was near uniformly depleted in 18O relative to the late Holocene. By contrast, tropical δ18O responses to deglacial warming diverged; late‐Pleistocene δ18O was higher‐than‐modern across India and South China but lower‐than‐modern throughout much of northern and southern Africa. Groundwaters that recharged beneath large northern hemisphere ice sheets have different Holocene‐Pleistocene δ18O relationships than paleowaters that recharged subaerially, potentially aiding reconstructions of englacial transport in paleo ice sheets. Global terrestrial late‐Pleistocene precipitation δ18O maps may help to determine 3‐D groundwater age distributions, constrain Pleistocene mammal movements, and better understand glacial climate dynamics.
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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 | 0.002 |
| 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) | 0.003 | 0.001 |
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".