Glacio-chemical signature of grain boundaries and insoluble particle aggregates in ice core 2D impurity imaging
Bibliographic record
Abstract
Identifying, understanding, and constraining post-depositional processes altering the original layer sequence in ice cores is especially needed in order to avoid misinterpretation of the oldest and most highly thinned layers. The record of soluble and insoluble impurities represents an important part of the paleoclimate proxy set in ice cores but is known to be affected post-depositionally through interaction with the ice matrix, diffusion and chemical reactions. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been recognized for its micron-scale resolution and micro-destructiveness in ice core impurity analysis. Important added value comes from employing LA-ICP-MS for state-of-the-art 2D chemical imaging. The latter has already revealed a close relationship between the ice grain boundary network and impurity signals with a significant soluble component, such as Na. Here we show the latest improvements in 2D chemical imaging of ice with LA-ICP-MS, by increasing the spatial resolution from 35 to 20 and even 10 µm and extending the simultaneous analysis to cover also mostly insoluble impurity species, such as Al. The latter reveal clear signals of insoluble particle aggregates in samples of Greenland ice cores. Combining the chemical images with computer vision-based image analysis allows to separate the geochemical signals of grain boundaries and insoluble particles. Considering intensities as well as elemental ratios, this classification further highlights important differences in the geochemical signals depending on the location of the impurities in the ice matrix. Ultimately, we discuss how this refined approach may serve to investigate post-depositional changes occurring with increasing depth to the soluble and insoluble impurity components, based on grain growth and chemical reactions, respectively.
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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 | 0.000 |
| 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".