River ice phenology and thickness from satellite altimetry. Potential for icebridge road operation
Bibliographic record
Abstract
Abstract. River ice is an important component of land cryosphere. Satellite monitoring of river ice is rapidly developing scientific area with an important outcome for many climate, environmental and socio-economic applications. Radar altimetry, now widely used for monitoring of river water regime, demonstrates a good potential for observation of river ice phenology and for an estimation of river ice thickness. Jason-2 and -3 Ku-band backscatter measurements are sensitive enough for detection of first appearance of the ice and of beginning of thermal ice degradation on the Lower Ob River (Western Siberia). Uncertainties of the altimetric ice events timing are less than 10 days for 88–90 % of cases. River ice thickness retrieved from altimetric measurements via empirical relations with in situ observations, has an accuracy (expressed as RMSE) varying from 0.07 to 0.18 m. We demonstrated that using satellite altimetry the dates of ice road opening at Salekhard city can be predicted quite accurately with 4 days delay. Uncertainties for the prediction of dates of the ice road closure are of 3 days with the delay varying from 4 days (for late melting start) to 22 days (for yearly melting start).
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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 | 0.001 | 0.001 |
| 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".