Spring sea-ice evolution in Rijpfjorden (80° N), Svalbard, from in situ measurements and ice mass-balance buoy (IMB) data
Bibliographic record
Abstract
Abstract Rijpfjorden (808 N, 22° E) is a high-Arctic fjord on Nordaustlandet in the Svalbard archipelago. To monitor the thermodynamic change of sea ice in spring, an ice mass-balance buoy (IMB) was deployed for 2.5 months (10 April–26 June 2011), with accompanying in situ measurements, sea-ice sampling on three occasions and ice-core analysis. Uncertainties and sources of error in in situ measurements and IMB data are discussed. The in situ measurements, ice-core analysis and IMB data together depict the development of snow and ice in spring. Snow and ice thickness exhibited large spatial and temporal variability. After relatively stable conditions with only little change in ice thickness and accumulation of snow, a layer of superimposed ice ∼0.06 m thick formed at the snow-ice interface due to refreezing of snow meltwater in late spring. Ice thickness (except for growth of superimposed ice) did not change significantly based on in situ observations. In contrast, the under-ice sonar data from the IMB show reflections from a layer deeper than the underside of the ice during the melting phase. This can be explained as a reflection of the sonar pulses from an interface between a freshwater layer under the ice and more saline water below, or as a false-bottom formation.
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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 | 0.001 | 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 | 0.001 |
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".