Bibliographic record
Abstract
Abstract Skeletonization is a syntactic pattern-recognition method that is applied to gridded data to produce an automatic line drawing, with an associated event catalog. Previous implementations of skeletonization have been tailored for seismic data. Here, we modify that technique to render it more suitable for other types of gridded data, with particular emphasis on aeromagnetic maps. A modification from previous schemes is the use of a two-pass approach, to reduce the effects of an otherwise problematic directional bias that discriminates against events oriented parallel to columns of the grid. The method can be used effectively for filtering aeromagnetic data on the basis of strike direction, event linearity, event amplitude, and polarity. It is based on the delineation of peak-trough pairs (cycles), which are traced throughout the grid to form contiguous events. Cycles and events are characterized by attributes that include amplitude, polarity, and pulse width. Events are further characterized by length, average strike direction, and linearity. The event attributes are stored in a catalog, thus enabling one to perform attribute-based analysis and data filtering. We illustrate our algorithm using two regional aeromagnetic examples from different parts of the Canadian Shield. The first, from the Great Slave Lake shear zone, is dominated by linear anomaly trends produced by faults and mafic dikes. The second, from the Manicouagan region of northeastern Quebec, contains abundant subcircular and arcuate anomaly patterns caused by large intrusive complexes and a meteorite impact structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".