GIS-based geospatial tools for estimating the magnetic anomaly depth of hydrothermal mineral deposits using inverse distance weights method
Bibliographic record
Abstract
The data used in this study consist of high-resolution airborne magnetic datasets for solid mineral exploration acquired across the Nigerian nationwide Terrains by the two Canadian firms awarded the contracts (a.g., Fugro Airborne Survey Services, and Patterson Grant and Watson), by the Nigerian Geological Survey Agency (NGSA). The Canadian firms had obtained the airborne magnetic data between 2003 and 2009, approximately along the NW–SE flight lines that were positioned at 90 degrees to the most significant narrow geological strike in the areas covered. Aircraft was flown at typical spaced of about half kilometers intervals to acquire the data, with a 2 km tie-line spacing along the northeast-southwest (NE-SW) directions at 80 m nominal flight elevation. The magnetometer settings were set at 0.1 s intervals to record the data. The combination of the nominal flight height—that was set exceptionally close to the ground surface using narrow line spacing—and the extremely small recording time gaps, helped to achieved a higher resolution of the magnetic anomalies than the general high-altitude airborne magnetic surveys. Prior to the data distribution by the Nigerian Geological Survey Agency, (NGSA) to the interested users, Fugro Airborne Surveys Company preprocessed the essential magnetic data corrections: the geomagnetic gradient was removed from the data using the existing model with the International Geomagnetic Reference Field (IGRF), January 2005 version, as specified in the World Geodetic System 1984 ellipsoid. The Universal Transverse Mercator (UTM) coordinate system was used to project the airborne magnetic data. The airborne magnetic survey data presented in this study covered the Omu-Aran Schist belt zone in parts of the Nigerian South-western Precambrian basement complex (NSPBC), with moderately shallow overburden lithologies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| 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) | 0.005 | 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".