Assessment of cleaning methods for electro-welded sieves to reduce/eliminate carry over contamination between till samples
Bibliographic record
Abstract
The Sedimentology Lab, with the support of the Geo-mapping for Energy and Minerals (GEM) and Targeted Geoscience Initiative (TGI4) Programs, set out to verify existing sieve cleaning methods used in geological sample preparation laboratories to ensure no cross contamination between samples. Three cleaning methods were tested using till samples with varying degrees of mineralization. The purpose of this study is to determine the effectiveness of commonly used cleaning methods at preventing cross contamination while sieving. This is critical to the quality of the geochemical analysis. Cleaning Method 1, while the quickest, does not remove contamination effectively for the two deposits sampled for this study. The addition of an extra step in Method 2 lowered the level of the potential contaminants without significant time increase. If known high mineralization exists, cleaning Method 3 is recommended otherwise Method 2 should be sufficient. In addition to sample preparation it would be a good practice, when submitting samples to be processed for geochemical analysis, to provide a processing order to the laboratory if they are aware of any potentially metal-rich samples. Metal-rich samples should be positioned at the end of the list to avoid any possible contamination due to sample preparation or analytical memory effect. Clean silica blanks should be inserted at the start of the batch and sieved blanks should be inserted to verify possible contamination in the preparation lab. The insertion of control reference materials (CRMs) and duplicates are also recommended for quality control.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".