Dye tracing of upward brine migration in snow
Bibliographic record
Abstract
The presence of salt in the snow overlying seasonal sea ice has profound thermodynamic and electromagnetic effects. However, the way that it arrives and is distributed within the marine snowpack remains poorly understood and modelled. We describe two experiments tracing upward brine movement in snow: one laboratory experiment at Rothera research station, West Antarctica, and a field experiment in Hudson Bay, Canada. The laboratory experiments involved the addition of dyed brine to the base of terrestrial snow samples, with subsequent wicking being characterised. After initial and total absorption at the base, the dyed brine migrated further up over nine days, ultimately reaching heights between 2.5 & 6 cm. Our field experiment involved dye being added directly (without brine) to bare sea ice and lake ice surfaces, with snow then accumulating on top over several days. On the sea ice, the dye migrated upwards into the snow by up to 5 cm as the snow's basal layer became more salty, whereas no dye migration occurred in our control experiment over lake ice. Upward dye migration and basal salinification over the sea ice occurred in relatively dry snowpacks where brine inclusions took up between 0.5 & 5.8% of the snow's pore volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".