Morphology and distribution of liquid inclusions in young sea ice as imaged by magnetic resonance
Bibliographic record
Abstract
Abstract. In order to determine the morphology and distribution of liquid inclusions in young sea ice, magnetic resonance imaging of an 18 cm sea ice core was done using a Siemens 3T TIM TRIO human scanner. The sample was stored at about −20 °C until the beginning of a constructive interference steady state gradient echo sequence which lasted four and a half min. Here we present the first three-dimensional reconstruction of a brine drainage channel network in young sea ice using magnetic resonance imaging. The magnetic resonance image sequence data presented here clearly illustrate that brine drainage channels are established relatively quickly during ice formation, and indicates the amount and location of vertical and horizontal fluid permeability in young sea ice. A simple analysis of the image sequence reveals that magnetic resonance imaging is useful in describing the vertical profile of liquid fraction that compares well to volumes calculated for similar sea ice temperatures. Future work in this vein may include three-dimensional magnetic resonance scans of sea ice cores at in situ temperatures using different magnetic resonance sequences in order to improve the observation of inclusions, though this will necessitate both access to a scanner and the construction of a cooling system compatible with a magnetic resonance imager.
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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 | 0.000 | 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) | 0.000 | 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".