A comparison of data from airborne, semi-airborne, and ground electromagnetic systems
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
Abstract The region around a small conductive massive sulfide body near Sudbury, Ontario, Canada, was used as a test site to compare airborne and ground electromagnetic (EM) systems with a new experimental EM system that uses a ground-based transmitter and an airborne receiver. In this test survey, the semi-airborne data were acquired with the transmitter loop used for the ground survey and the receiver normally used for the airborne system. At the time the data were acquired, there was no synchronization between the semi-airborne receiver and the ground transmitter. However, subsequent digital processing of the full waveform data allowed the zero-time position to be defined. The data could then be stacked and windowed. The ratio of the peak signal to the late-time noise level for the airborne data is about 25:1, the semi-airborne data shows signal-to-noise ratios of 500:1, while the signal-to-noise ratio for the ground data has a ratio of 50 000:1. This particular conductor is very close to the ground transmitter and receiver, so the signal-to-noise ratio for the ground system is very high. Numerical modeling shows that the marked advantage of the ground system is reduced when the conductor is deeper. However, the semi-airborne system will generally show signal-to-noise intermediate between the airborne and ground systems. From an operational perspective, the semi-airborne system has features of both the ground and airborne systems. Like the ground system, it is necessary to lay a transmitter loop on the ground; but because an aircraft is used, the semi-airborne receiver can cover the survey area much more quickly.
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 | 0.001 |
| 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 it