Creating high-fidelity images – a convolution approach to gridding airborne data using a SINC kernel
Bibliographic record
Abstract
Airborne geophysical data sampled at a constant time-interval along lines and with a nominal spatial-separation across lines are quasi-periodic and are therefore amenable to interpolation onto a regular grid by convolving the original line data with a 2D SINC kernel. This method has advantages over commonly used minimum curvature or bidirectional gridding algorithms because it better maintains the spatial fidelity of the original data as per the sampling theorem. Unlike splines, the SINC function does not suffer from over-interpolation, although ringing can be problematic. Lastly, the smooth SINC kernel places no lower bound on cell sizes, except computation time.The method first uses a 1D SINC interpolation to resample the along-line data. An SVD matrix inversion is then used to move each line from its true to its nominal location before interpolating the inter-line areas of the grid with the 2D kernel. Ringing and aliasing are mitigated by employing a window function to dampen the effects of the ideal RECT function in the wavenumber domain. Through judicious selection of the along- and across-line maximum wavenumbers, the SINC kernel can improve the continuity of lineaments trending obliquely to the flight lines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".