Bibliographic record
Abstract
An inversion approach is presented that determines the three‐dimensional (3D) susceptibility distribution that produces a given magnetic anomaly. The subsurface model comprises a 3D, equally‐spaced array of dipoles. The inversion incorporates a model norm that enforces sparseness and depth‐weighting of the solution. Sparseness is imposed by using the Cauchy norm on the model parameters. This constrains the resulting model to be simple, with no excessive structure. The inverse problem is posed in the data space, leading to a linear system of equations with dimensions based on the number of data, N. This contrasts the standard least squares solution, derived through operations within the M‐dimensional model space (M being the number of model parameters). Hence, the data‐space method leads to computational efficiency by dealing with an N×N system versus an M×M one, where N≪M. Inversion of aeromagnetic data collected over a Precambrian Shield area shows that including the sparseness constraint leads to a simpler and better resolved solution
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 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) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".