Impulsive moments at work
Bibliographic record
Abstract
The moments of the impulse response are a new tool for the interpretation of transient electromagnetic data. The rath moment is the integral of the impulse response weighted by time to the rath power. The zeroth- and first-order moment are equivalent to the inductive and resistive limits and the higher-order moments place emphasis on the late time data.A good approximation to the moments can be calculated relatively easily from the measured data. Also, for some simple models, the formulae for the moments are relatively simple expressions. Hence, it is comparatively easy to invert these expressions to derive source parameters from the measured moments. For example, the conductance of a thin sheet or the conductivity of a halfspace can be derived from the low-order moments. There are no analytic expressions for the high-order moments of a half-space, so the concept of realizable moments has been introduced to allow the higher-order realizable moments to be converted to a conductivity or conductance estimate.The moment data have been shown numerically to be additive. This has two ramifications. 1) The earth can be approximated by a multiplicity of small spheres and the properties of these spheres can be inverted for. A prototype-imaging scheme which does this gives promising results. 2) The regional or background response can be subtracted from the data and the residual anomaly can be modelled. Because the modelling algorithms are fast, they can be incorporated into inversion schemes linked to database packages such as Geosoft montaj.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".