The AeroTEM airborne electromagnetic system
Bibliographic record
Abstract
Airborne electromagnetic (EM) systems have evolved into two basic platforms since their introduction in the 1950s. The helicopter-borne frequency-domain systems (HEM) use narrow-band, low-moment transmitters and closely spaced receivers with a rigid geometry between the transmitter and receiver coils. A wide range of conductance discrimination, excellent spatial resolution, and moderate depth penetration characterizes these systems. The fixed-wing time-domain systems (AEM) use wide-band, high-moment transmitters and separated receiver coils in a geometry that is not rigid. These systems have a moderate range of conductance discrimination, moderate spatial resolution, and much greater depth penetration than HEM systems. Since 1995 there have been a number of attempts at adapting the advantages of the fixed-wing time-domain systems to the helicopter platform. The AeroTEM system, the result of one such effort, is based on a rigid, concentric-loop geometry with the receiver coils placed in the center of the transmitter loop (Figure 1). The advantages of this configuration include: maximum coupling to all target geometries regardless of the depth below the surface; sharper anomalies with simpler shapes compared to fixed-wing systems; anomaly shapes independent of the flight-line direction; and coincident transmitter-receiver coils have lower sensitivity to conductive overburden than separated transmitter-receiver systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".