Non-ergodic approximation method for intersections of airborne gravity survey network
Bibliographic record
Abstract
The gravity difference at the intersections of the airborne gravity survey network is an important basis for objectively evaluating the measurement quality of the survey lines, and it is alsoan important way to adjust the level difference of the gravity field between the survey lines. With the enlargement and irregularity of the survey network, it is very important to search the intersections accurately and quickly. Existing methods all traverse one by one after narrowing the range of intersections, which cannot guarantee that all intersections can be searched quickly and accurately. The non-ergodic approximation method proposed in this paper is to perform iterative approximation through a combination of fast approximation and fine-tuning approximation, avoiding one-by-one traversal and directly approaching the intersections quickly. Experimental results show that this method is not only suitable for continuous and uniform conventional networks, but also suitable for irregular and unconventional networks. The search speed is 3~4 orders of magnitude higher than the existing methods, and it is also far superior than the famous Canadian commercial geophysical software of Oasis Montaj.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".