A comparison of satellite-derived sea-ice motion with drifting-buoy data in the Weddell Sea, Antarctica
Bibliographic record
Abstract
Abstract We compare a satellite-derived sea-ice motion dataset obtained from the US National Snow and Ice Data Center with daily ice drift by drifting buoys between 1989 and 2005. the satellite data were derived from daily composites of passive-microwave satellite measurements by means of a cross-correlation method and were supplemented with data from visible and thermal channels of the Advanced Very High Resolution Radiometer. Seasonal and interannual variations of the agreement between the two datasets are discussed. In addition, regional differences in the agreement and correlation coefficients of buoy- and satellite-derived drift components are analyzed. Results show that the overall drift regime can be well described by satellite-derived drift data but 71% of the retrieved drift velocities are lower than those observed by buoys. Nevertheless, correlation coefficients, r, between the two datasets are 0.587 for the zonal and 0.613 for the meridional drift component. the correlation coefficients between monthly averages of buoy- and satellite-derived zonal and meridional drift components are on average 25.7% and 16.4% lower in summer (October–February) than in winter (March–September), with the exception of January. In January, correlation coefficients are about 62.6% (zonal) and 66.5% (meridional) lower than in winter. Furthermore, deviations between zonal buoy- and satellite-derived drift are 80% larger in the second half than in the first half of the year. the observed yearly and regional averaged agreement between the two datasets depends strongly on the season when buoy data were collected and on the number of coincident buoy and satellite data, which was often very low.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".