A High-Resolution Continuous Flow Analysis System for Polar Ice Cores
Bibliographic record
Abstract
In recent decades, the development of continuous flow analysis (CFA) technology for ice core analysis has enabled greater sample throughput and greater depth resolution compared with the classic discrete sampling technique. We developed the first Japanese CFA system at the National Institute of Polar Research (NIPR) in Tokyo. The system allows the continuous analysis of stable water isotopes and electrical conductivity, as well as the collection of discrete samples from both inner and outer parts of the core. This CFA system was designed to have sufficiently high temporal resolution to detect signals of abrupt climate change in deep polar ice cores. To test its performance, we used the system to analyze different climate intervals in ice drilled at the NEEM (North Greenland Eemian Ice Drilling) site, Greenland. The quality of our continuous measurement of stable water isotopes has been confirmed through a comparison with different datasets. Moreover, our system presents a better measurement efficiency to resolve the signal of electrical conductivity in the ice core sample than that obtained with a similar system developed by the University of Bern, Switzerland, during a field campaign at NEEM camp.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".