A recursive approach to Multiscalar Data Interpolation of sparsely sampled sea surface measurements at different spatial resolutions
Bibliographic record
Abstract
In many oceanographic studies there is a need to reconstruct a signal from a set of sparse measurements. We propose an algorithm to iteratively approximate the intermediate values between irregularly sampled data, when a set of sparse values at coarser scales is known. This is possible when there is an approximation to a model for the multiresolution decomposition/reconstruction scheme of the dataset. Although the problem is ill-posed, this approach gives an easy scheme to interpolate the values of a signal using all the information available at different resolutions. This reconstruction method could be used as an extension of any interpolation method to optimize the multiresolution sparse data fusion. A simplified one-dimensional case illustrates the explanation; it is an algorithm based on a recursive scheme of a fast dyadic wavelet transform and its inversion, using a filter bank analysis/synthesis implementation for the wavelet transforms model. This can be a basis method suitable for applied cases where there are sparse measures from different instruments that are sensing the same scene simultaneously with several resolutions. Extensions of the method to sparse multiresolution dataset with higher dimensions (images or vector fields) also offer some promising preliminary results.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".