Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Z-Axis Tipper Electromagnetic Technique (ZTEM) data are airborne electromagnetic data which record the vertical magnetic field that results 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. The transfer functions depend on frequency and provide information about the 3-D conductivity structure of the Earth. The practical frequency range is 30–720 Hz and hence it is possible to see structures at depths of a kilometre or more if the earth is of moderate conductivity. This depth of penetration is significantly greater than that obtained with controlled source EM techniques and, when coupled with rapid spatial acquisition with an airborne system, means that ZTEM data can be used to map large-scale structures that are difficult to survey with ground based surveys. We present some fundamentals about understanding the signatures obtained with ZTEM transfer functions and then develop a Gauss–Newton algorithm to invert ZTEM data. The algorithm is applied to synthetic examples and to a field data set from the Bingham Canyon region in Utah. The field data set requires a workflow procedure to estimate appropriate noise levels in individual frequency components. These noise levels can then be used to invert multiple frequencies simultaneously. ZTEM data are insensitive to a 1-D conductivity structures and hence the background can be difficult to estimate. We provide two methods to determine appropriate background models. Interestingly, topography, which is usually a hinderance in field data interpretation, provides a first-order signal in the ZTEM data and helps with this calibration.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it