Preliminary on-ice remote sensing measurements during the MOSAiC expedition
Bibliographic record
Abstract
Several different remote sensing instruments were deployed on the sea ice floe next to RV Polarstern during the MOSAiC expedition (mosaic-expedition.org). Here, preliminary data from nine instruments for two observation periods (Nov 2019 and Sep 2020) is provided. Initial calibration was performed but data might change for the final datasets. Outliers were filtered and some time series smoothed. Data from Figure 10 in Nicolaus et al. (2021), "Overview of the MOSAiC expedition – Snow and Sea Ice", Elementa: Results from co-located active and passive remote sensing instruments (Table 2) looking at similar ice and snow conditions (Figure S4). (left) Measurements during a warming and storm event in November 2019 and (right) during a melting event in September 2020. (A) Air temperature and wind speed from the Polarstern weather station and snow surface temperature from the IR camera at the Remote Sensing Site (dashed blue line shows time periods with potential icing on the lens). (B) Radar backscatter at VV polarization from 2145 microwave scatterometers L-SCAT at 1.3 GHz and Ku/Ka-radar at 15 and 35 GHz (note the different y-scales). (C) Brightness temperature at V polarization from microwave radiometers: ELBARA at 1.4 GHz, ARIEL at 1.4 GHz looking at thin ice on a lead, HUTRAD at 7 and 11 GHz, SSMI at 19, 37, 89 GHz (not all available data shown). (D) Reflected GNSS data, i.e., reflectivity at the Remote Sensing Site (blue) and for sea ice next to Polarstern (red). In the plot titles the used incidence angle range is given. Vertical dashed lines mark the start of warming and/or storm events. (E) Exemple photographs of the remote sensing site during winter and summer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".