South Pole glacial climate reconstruction from multi-borehole laser particulate stratigraphy
Bibliographic record
Abstract
Abstract The IceCube Neutrino Observatory and its prototype, AMANDA, were built in South Pole ice, using powerful hot-water drills to cleanly bore>100 holes to depths up to 2500 m. The construction of these particle physics detectors provided a unique opportunity to examine the deep ice sheet using a variety of novel techniques. We made high-resolution particulate profiles with a laser dust logger in eight of the boreholes during detector commissioning between 2004 and 2010. The South Pole laser logs are among the most clearly resolved measurements of Antarctic dust strata during the last glacial period and can be used to reconstruct paleoclimate records in exceptional detail. Here we use manual and algorithmic matching to synthesize our South Pole measurements with ice-core and logging data from Dome C, East Antarctica. We derive impurity concentration, precision chronology, annual-layer thickness, local spatial variability, and identify several widespread volcanic ash depositions useful for dating. We also examine the interval around ∼74 ka recently isolated with radiometric dating to bracket the Toba (Sumatra) supereruption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".