Imaging air volume fraction in sea ice using non-destructive X-ray tomography
Bibliographic record
Abstract
Abstract. Although the presence of a gas phase in sea ice creates the potential for gas exchange with the atmosphere, the distribution of gas bubbles and transport of gases within the sea ice are still poorly understood. Currently no straightforward technique exists to measure the vertical distribution of air volume fraction in sea ice. Here, we present a new fast and non-destructive X-ray computed tomography technique to quantify the air volume fraction and produce separate images of air volume inclusions in sea ice. The technique was performed on relatively thin (4–22 cm) sea ice collected from an experimental ice tank. While most of the internal layers showed air volume fractions < 2 %, the ice–air interface (top 2 cm) systematically showed values up to 5 %. We suggest that the air volume fraction is a function of both the bulk ice gas saturation factor and the brine volume fraction. We differentiate micro bubbles (Ø < 1 mm), large bubbles (1 mm < Ø < 5 mm) and macro bubbles (Ø > 5 mm). While micro bubbles were the most abundant type of gas bubbles, most of the air porosity observed resulted from the presence of large and macro bubbles. The ice texture (granular and columnar) as well as the permeability state of ice are important factors controlling the air volume fraction. The technique developed is suited for studies related to gas transport and bubble migration.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".