Bibliographic record
Abstract
Abstract Euler deconvolution of magnetic fields, induced by sheets with nonnegligible widths, provides source-location estimates that are biased away from the true locations. I have derived formulas for these biases and used the equations to model diffuse solution patterns that are owing to the interplay between integer structural indices and finite sources. These patterns closely match solutions deconvolved from aeromagnetic data over northern Canada. Motivated out of the necessity that complete harmonics be integral degreed, I have investigated and discovered the ineffectiveness of noninteger structural indices in remediating the aforementioned biases. In fact, real numbers impart similar errors to multiple Euler solutions, causing ensembles of estimated origin loci to reside below the middle of the tops of wide sheets. I have devised an approach requiring the inclusion of a term in the Euler deconvolution kernel whose independent variable is the second horizontal derivative of the total field, and whose partial slope (to be solved) is the sheet width. This approach is appropriate only if the thickness does not exceed the depth. However, precision could be sacrificed in favor of accuracy because of the presence of the second derivative. The application to aeromagnetic data over a diabase dike in northern Canada yields a depth effectively coincident with a drilling depth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".