Space/Time Statistics of Polar Ice Motion
Bibliographic record
Abstract
Ice motions have been computed from passive microwave imagery (SMMR and SSM/I) on a daily basis for both Polar Regions. In the Arctic these daily motions have been merged with daily motions from AVHRR imagery and the Arctic buoy program. In the Antarctic motion only from the AVHRR were available for merging with the passive microwave vectors. Long-term means, monthly means and weekly means have all been computed from the resulting 22-year time series of polar motion. Papers are in preparation that present the long term (22 year) means, their variability and show animations of the monthly means over this time period for both Polar Regions. These papers will have links to enhanced objects that allow the reader to view the animations as part of the paper. The first paper presents the motion results from each of the Polar Regions. The second paper looks only at motion in the Arctic in order to develop a time series of in the Arctic. Starting with the first full SMMR year in 1979 we keep track of each individual ice element (resolution of the sensor) and track it in the subsequent monthly time series. After a year we age each particle and we thus can keep track of the of the starting in 1979. We keep track of classes between one and five years and thus we can see the evolution of the as it ages after the initial 5-year period. This calculation shows how we are losing the older through Fram Strait at a rather alarming rate particularly in the past 15 years. This loss of older has resulted in an overall decrease in the thickest, oldest ice, which is now limited to a region just north of the Canadian Archipelago with tongues extending out across the pole towards the Siberian Shelf. This loss of old is consistent with the effects of global warming which provides the heat needed to melt, move and disperse this oldest through Fram Strait. This is the first step in a progression that may eventually open the Arctic pack and lead to an ice-free Arctic Ocean.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".