Neural Networks Based Sea Ice Detection and Concentration Retrieval From GNSS-R Delay-Doppler Maps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a neural networks (NN) based scheme is presented for detecting sea ice and retrieving sea ice concentration (SIC) from global navigation satellite system reflectometry delay-Doppler maps (DDMs). Here, a multilayer perceptron neural network with back-propagation learning is adopted. In practice, two NN were separately developed for sea ice detection and concentration retrieval purposes. In the training phase, DDM pixels were employed as an input. The SIC data obtained by Nimbus-7 SMMR and DMSP SSM/I-SSMIS sensors were used as the target data, which were also regarded as ground-truth data in this paper. After the training process using a dataset collected around February 4, 2015, these networks were used to produce corresponding detection and concentration estimation for other four sets of DDM data, which were collected around February 12, 2015, February 20, 2015, March 16, 2015, and April 17, 2015, respectively. Results show high accuracy in sea ice detection and concentration estimation with DDMs using the proposed scheme. On average, the accuracy for sea ice detection is about 98.4%. In terms of estimated SIC, the mean absolute error is less than 9%, whereas the correlation coefficient is as high as 0.93 compared with the reference data. It was also found that low sea state and wind speed could lead to an overestimation of SIC for partially ice-covered region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it