Processing of glacial sediments for the recovery of indicator minerals: protocols used at the Geological Survey of Canada
Bibliographic record
Abstract
A successful method of mineral exploration in glaciated terrain is the use of indicator minerals recovered from carefully selected glacial sediments, and subsequently traced back to their bedrock source. The successful application of indicator mineral methods relies on efficient and effective recovery as well as the correct identification of a wide variety of indicator minerals. The Geological Survey of Canada (GSC) has developed protocols for ongoing and future research projects to achieve the highest quality for reporting indicator mineral data. Such protocols include the use of field duplicate samples, blank samples, and base material spiked with known numbers, morphologies, species, and sizes of indicator minerals. Field duplicate samples serve to estimate sediment heterogeneity. Spiked samples are used to monitor the accuracy of the sample processing and mineral identification methods for recovering specific minerals. Blank samples serve to detect potential carry-over contamination. In certain instances, a specific sample processing order is essential and should be communicated to the commercial processing laboratory. Ore-rich samples collected near known mineralization are to be processed last, to reduce chances of carry-over contamination. Repeated indicator mineral counts should be carried out on at least 10% of the heavy mineral concentrates to measure reproducibility (precision) of the mineral counts. All indicator mineral data, original laboratory reports, heavy mineral concentrates, unmounted picked grains, and grain mounts are now archived at the GSC, using specific guidelines.
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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 | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".