Airborne electromagnetic data levelling based on the structured variational method
Bibliographic record
Abstract
Abstract. Levelling errors are defined as the data difference among flight lines in airborne geophysical data. The differences in the signal levelling always appear as a striping pattern parallel to the flight lines on the imaged maps. The fixed structured pattern inspires us to structure a guided levelling error model using an anisotropic Gabor filter. We then embed the levelling error model into a total variational framework to flexibly calculate levelling errors. The guided levelling error model constrains the noise term of total variation rather than just using blind removal. Moreover, we can also apply the structured variational method to remove other noises in airborne geophysical data. This would just require replacing the noise prior models in the proposed method. We have applied this method to the airborne electromagnetic, magnetic, and apparent conductivity data collected by the Ontario Geological Survey to confirm its validity and robustness by comparing the results with the published data. The structured variational method can better level the airborne geophysical data based on the space properties of the levelling error.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".