Distribution of air bubbles in the EDML and EDC (Antarctica) ice cores, using a new method of automatic image analysis
Bibliographic record
Abstract
Abstract Air bubbles in ice cores play an essential role in climate research, not only because they contain samples of the palaeoatmosphere, but also because their shape, size and distribution provide information about the past firn structure and the embedding of climate records into deep ice cores. In this context, we present profiles of average bubble size and bubble number for the entire EDML (Antarctica) core and the top 600 m of the EDC (Antarctica) core, and distributions of bubble sizes from selected depths. The data are generated with an image-processing framework which automatically extracts position, orientation, size and shape of an elliptical approximation of each bubble from thick-section micrographs, without user interaction. The presented software framework allows for registration of overlapping photomicrographs to yield accurate locations of bubble-like features. A comparison is made between the bubble parameterizations in the EDML and EDC cores and data published on the Vostok (Antarctica) ice core. The porosity at the firn/ice transition is inferred to lie between 8.62% and 10.48% for the EDC core and between 10.56% and 12.61 % for the EDML core.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".