A FRAMEWORK FOR ROCK PROPERTY DATA ACQUISITION, VISUALIZATION AND ANALYSIS: AN EXAMPLE FROM THE BATHURST MINING CAMP, NORTHERN NEW BRUNSWICK
Bibliographic record
Abstract
Petrophysical studies provide the link between geophysical grids and interpretive geological maps. In this study we assess the reliability of some physical property measurements: density, magnetic susceptibility, IP/Resistivity, and conductivity. Comparing susceptibility measurements obtained with different instruments shows there is a need for careful selection of sensor coil frequency and use of a suite of reference standards. For density measurements we show more reliable estimates require the sensitivity of the weighing scale be adjusted relative to sample weight and that a vacuum saturation tank is needed when the sample may have significant porosity. Galvanic resistivity measurements on hand samples are limited by the ability to properly estimate the effective cross-sectional area. Inductive conductivity instruments generally have a limited sensitivity range. We recommend the study of physical properties based on the concept of populations associated with stratigraphic units, rather than based on lithology. We present a suite of data visualizations which highlight different aspects of the sampled lithologies. Cross plots, ordered data plots, and violin plots permit the recognition of populations, and outliers. Henkel plots using templates with known mineralogical input parameters allow the identification of geological factors such as enhanced feldspar content, serpentinization, alteration, etc. Analysis of the sample data from the Bathurst Mining Camp, New Brunswick reveals which units have sufficient petrophysical contrast to permit pseudo-geological mapping. More specifically, not all basalts have high susceptibility and low density; it is impossible to discriminate between the various packages of rhyolites, and there is little difference between the granites sampled.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".