PMTools – a new program for paleomagnetic data analysis and visualization
Bibliographic record
Abstract
There are many programs for the analysis and visualization of paleomagnetic data, but each of them is good only in a certain use case and does not allow to perform a full cycle of paleomagnetic operations. Therefore, one has to resort to using a number of programs to complete the full path of processing paleomagnetic data. You often have to convert data from one format to another, manually vectorize charts, and generally spend more time and effort than could theoretically be spent. Thus, there is a long overdue need for a universal program capable of fast, convenient and high-quality performance of a full cycle of paleomagnetic operations. A set of programs written by Randy Enkin (Enkin, 1996) for DOS was taken as a time-tested example of such a program. The choice fell on them, since these programs (although they are very outdated) allow performing a full cycle of paleomagnetic operations and do it as conveniently and efficiently as possible for that time. Our goal is to create a program devoid of all of the above disadvantages and capable of developing indefinitely as modular opensource software by the efforts of all people interested in this. The result of our work is PMTools – a cross-platform software for statistical analysis and visualization of paleomagnetic data. PMTools supports all widely used paleomagnetic data formats and allows you to work with them simultaneously. All charts created in PMTools are vector, adapted for direct using in publications and presentations, and can be exported in both vector and raster formats. At the same time, PMTools implements a full cycle of routine paleomagnetic operations: from finding the best-fit directions to calculating the mean paleomagnetic poles. Moreover, all operations can be performed both with a mouse through a graphical user interface and with hotkeys, which significantly speeds up the data analysis process. In the near future, PMTools will become a modular open source application, so that each user will be able to add its own modules, thereby expanding the program's functionality. References Enkin, R.J., 1996. A Computer Program Package for Analysis and Presentation of Paleomagnetic Data, Pacific Geoscience Center, Geological Survey of Canada, http://www.pgc.nrcan.gc.ca/tectonic/enkin.htm.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.166 | 0.103 |
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".