Grade Estimation At Cvrd Inco’S Canadian Sulphide Mines
Bibliographic record
Abstract
The paper provides an overview of grade estimation at CVRD Inco Limited’s (CVRD Inco) Canadian sulphide mines. The main Ni grade estimation technique used is blasthole conductivity. This technique is applicable in zones where the sulphide is sufficiently well connected to provide eddy current responses in inductive conductivity probes. In zones where the sulphide content is low (less than 25-30%) and the degree of sulphide connectivity is insufficient to cause reliable eddy current responses in inductive conductivity probes, magnetic susceptibility is being employed to enhance Ni grade estimation. In these zones, magnetic susceptibility complements conductivity-based grade estimation because of the monoclinic or magnetic pyrrhotite content of the sulphide. Additional techniques being investigated for grade estimation and discrimination between chalcopyrite-rich and pyrrhotite-rich mineralization include resistivity, natural gamma and density. The paper concludes with a summary of the nuclear-based PGNA (Prompt Gamma Neutron Activation) technique. This is a more direct grade determination technique that employs a neutron generator and gamma detectors. The resulting gamma ray spectra contain information about elemental concentrations including Ni, Cu, Co, Fe, S, Si, Ca, Al, etc.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".