Chemical impurity distribtion (Na, Mg, Sr) in the EPICA Dome C ice core bag 1065 obtained from 2D imaging with LA-ICP-MS
Bibliographic record
Abstract
<p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers micron-resolution 2D chemical imaging, which has been adapted recently to ice core analysis. Measurements were performed in 2020 at the Ca’Foscari University of Venice, in order to investigate the localization of impurities in the ice samples. Here an image is presented from applying LA-ICP-MS elemental imaging to a glacial (MIS2, bag 1065) samples of the EPICA Dome C ice core from central Antarctica. Lateral resolution is 35 microns both along and perpendicular to the scan direction. Considered as analytes are 23Na, 25Mg and 88Sr. Background and drift correction as well as image construction were performed using the software HDIP (Teledyne Photon Machines, Bozeman, MT, USA). Impurity images are acquired as a pattern of lines, without overlap in the direction perpendicular to that of the scan, and without any further spatial interpolation. Each pixel in an ice core chemical image has a size of 35 μm x 35 μm. For each chemical element the datasets comprise a numerical matrix which contains rows and columns according to the physical size of the image: an image of 7 mm x 35 mm in size has 200 rows and 1000 columns. The numerical entries in this matrix refer to the recorded intensity (e.g. in counts). Values lower than the detection limit are set to zero. Due to the careful synchronization, the individual pixels of the different chemical channels can be considered to be almost perfectly spatially aligned. In contrast, the mosaic of visual images obtained from the laser camera is not a-priori aligned with the chemical images. The visual images are generally characterized by air bubbles (dark blobs), grain boundaries (dark lines) and occasional sub-grain boundaries (thin dark lines).</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".