CROSS-HOLE RADIO IMAGING STUDY, MACLENNAN TOWNSHIP
Bibliographic record
Abstract
The Nickel Rim South property is located entirely within MacLennan Township, 22 kilometers northeast of Sudbury, Ontario, Canada. The occurrence of conductive sulphides in an otherwise highly resistive host rock makes it an ideal place for exploring and mapping using high frequency electromagnetic methods. The FARA radio imaging (RIM) system was deployed to explore between two proximal boreholes about 100 m apart. Tomographic data were collected at 0.625 and 1.250 MHz. Reciprocal data were also collected, with the transmitter and receiver transferred from one hole to the other. The ImageWin data processing software was used to reduce and edit the data and then to generate attenuation tomograms from the amplitude data. The SIRT algorithm was used for calculating the tomograms. “Weight clamping” was imposed to preserve low attenuation ray paths. Separate tomograms have been created for the original and reciprocal data sets. Finally, the two sets of attenuation coefficients were averaged and imported into Geosoft to create a final tomogram for the panel. There was good qualitative agreement between the ImageWin tomograms and an independent set generated by FARA. However, the FARA tomograms are much smoother. The differences in appearance will be investigated further. Resistivity values were calculated from the attenuation coefficients and are in good agreement with FARA resistivity results. We intend to combine the RIM tomograms with the results from borehole electromagnetic (BHEM) data and physical rock properties derived from wireline borehole logging. Interpreting RIM in conjunction with other data sets will help geophysicists develop a better understanding of the RIM method and its application to geophysical exploration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".