Bibliographic record
Abstract
SummaryAs the number of near surface deposits decreases, it becomes increasingly important to develop geophysical techniques to image at depth. Because of the penetration advantage of plane wave natural sources, these techniques are ideal to answer questions about the deep subsurface to the earth. A ZTEM survey is an airborne electromagnetic survey which records the vertical magnetic field that result from natural sources. The data are transfer functions that relate the local vertical field to orthogonal horizontal fields measured at a reference station on the ground. While the airborne nature of the survey means that large survey areas can be surveyed quickly and economically, the high number of cells required to discretize the entire survey area at reasonable resolution can make the computational costs of inverting the entire data set all at once prohibitively expensive. Here we present a workflow methodology that can be used to invert large natural source surveys by decomposing the large inverse problem into smaller more manageable problems before combining the tiles into a final inversion result. We use the procedure to invert synthetic ZTEM data for the Noranda mining camp as well as a field data example. Both of these data sets were far too large to solve on a single grid even with multiple processors at our disposal.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".