Bibliographic record
Abstract
Abstract Separating the fields produced by sources at different depths is a common requirement in the interpretation of potential field data. Approaches to this problem are generally data- or model-based. Data-based methods require clear linear segments in the logarithmic power spectrum of the data corresponding to different components of the field. Various types of filters can then be designed to carry out the separation. When the logarithmic power spectrum shows no identifiable linear spectral segments, other approaches are necessary. We outline a model-based method that does not depend on power-spectral information but requires independent estimates of the average depths of the source distributions, e.g., from seismic interpretations. An ensemble of models using fractal source distributions is computed based on these known values, and filter parameters are determined that produce the closest fit (in a least-squares sense) to the theoretical fields that each source distribution generates. This approach is used to separate basement effects from intrasedimentary sources in magnetic data collected over the Colville Hills, Northwest Territories, Canada. Seismic data interpretation places crystalline basement at ∼10 km depth and an intrasedimentary basaltic layer at ∼2 km. Our approach results in an optimal separation filter with a cutoff wavelength of ∼12 km that appears to provide an effective separation of the two source effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".