Estimation of depth and shape factor from potential‐field data over sources of simple geometry
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2002Estimation of depth and shape factor from potential‐field data over sources of simple geometryAuthors: Ahmed SalemDhananjay RavatMartin MushayandebvuKeisuke UshijimaAhmed SalemKyushu U., Higashi‐ku, Fukuoka, Japan, Dhananjay RavatSouthern Illinois U., Carbondale, Il, Martin MushayandebvuImage Interpretation Technologies Inc, Calgary, Alberta, Canada, and Keisuke UshijimaKyushu U., Higashi‐ku, Fukuoka, Japanhttps://doi.org/10.1190/1.1817358 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Permalink: https://doi.org/10.1190/1.1817358FiguresReferencesRelatedDetailsCited byA new approach automatic separation of the Bouguer gravity anomaly, using a new concept for 2D-semi-inversion of the sphere-shaped model26 July 2022 | NRIAG Journal of Astronomy and Geophysics, Vol. 11, No. 1New Semi-Inversion Method of Bouguer Gravity Anomalies Separation14 December 2022Inversion of the amplitude of the two-dimensional analytic signal of the magnetic anomaly by the particle swarm optimization technique21 June 2010 | Geophysical Journal International, Vol. 182, No. 2SCALFUN: 3D analysis of potential field scaling function to determine independently or simultaneously Structural Index and depth to sourceMaurizio Fedi and Giovanni Florio6 October 2006 SEG Technical Program Expanded Abstracts 2002 ISSN (print):1052-3812 ISSN (online):1949-4645 Copyright: 2002 Pages: 2478 publication data© 2002 Copyright © 2002 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 03 Jan 2005 CITATION INFORMATION Ahmed Salem, Dhananjay Ravat, Martin Mushayandebvu, and Keisuke Ushijima, (2002), "Estimation of depth and shape factor from potential‐field data over sources of simple geometry," SEG Technical Program Expanded Abstracts : 724-726. https://doi.org/10.1190/1.1817358 Plain-Language Summary PDF DownloadLoading ...
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".