Bibliographic record
Abstract
Discrete conductor models like sphere and plate were introduced in the 1950s as modelling tools in airborne electromagnetic (AEM) survey interpretation. In the last 20 years, with the development of inversion techniques, they have been integrated into parametric inversion programs. The recent advent of powerful workstations makes them useful tools for interactive AEM interpretation. Different problems have been encountered in the implementation and application of discrete objects as modelling and inversion tools. The sphere response is modelled using a sum of spherical functions. Assuming that the radius of the sphere is small compared to the distance between the transmitter and receiver to the centre of the sphere, the response can be approximated by using only the first term of the solution. This approach is reviewed for modelling the response of a conductive sphere in free space or buried in a layered earth. Plate modelling is based on spectral methods or the integral equation method, which provide different techniques for estimating the response of a plate in free space. A comparison of the results of these techniques show differences attributed to the different discretisation methods. A case history from Abitibi, Canada, shows that plate inversion using two different inversion methods provides useful information when the target is a plate-like conductor in a resistive environment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".