PlateCurie: Software for mapping Curie depth from a wavelet analysis of magnetic anomaly data
Bibliographic record
Abstract
Crustal magnetic anomalies carry information on the source distribution of magnetization in the Earth's crust (Blakely, 1995). The Curie point corresponds to the depth at which crustal rocks loose their magnetization where they reach their Curie temperature, and is obtained by fitting the power spectral density (PSD) of magnetic anomaly data with a model where magnetic anomalies are confined within a layer (Bouligand et al., 2009; Audet and Gosselin, 2019; Mather and Fullea, 2019). Mapping the Curie point provides important information on geothermal gradients in the Earth; however, mapping Curie depth is a spatio-spectral localization problem because the PSD needs to be calculated within moving windows at wavelengths long enough to capture the greatest possible depth to the bottom of the magnetic layer. The wavelet transform is particularly well suited to overcome this problem because it avoids splitting the grids into small windows and can therefore produce PSD functions at each point of the input grid (Gaudreau et al., 2019). This package extends the package plateflex, which contains python modules to calculate the wavelet transform and scalogram of 2D gridded data, by providing a new class MagGrid that inherits from plateflex.classes.Grid with methods to estimate the properties of the magnetic layer (depth to top of layer (zt), thickness of layer (dz), and power-law exponent of fractal magnetization (β)) using Bayesian inference. Common computational workflows are covered in the Jupyter notebooks bundled with this package. The software contains methods to make beautiful and insightful plots using the seaborn package.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.029 |
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".