Discussion on Quality Control of Basic Analysis Samples about Solid Mineral Exploration Abroad
Bibliographic record
Abstract
Along with the strategy ofopeningand going globein Chinese mining industry,more and more solid mineral exploration projects must be abided by the quality-control system authoried by the western security commissions.This paper was sketched the basic controlling factors on the basis of analysis sample.The systematic quality-control methods for the basic assay samples during the solid mineral exploration are introduced minutely.During mineral deposit exploration,sampling quality is monitored through twin samples and field duplicate;preparation quality is assessed through coarse blanks and coarse duplicates which are inserted in sample batch prior to or during preparation.Assaying quality is monitored through SRMs,fine blanks,pulp duplicates that are inserted in the sample batch prior to assaying.By using the quality-control systematic methods for the bacic assay samples mentioned above to the practice of the guangdong Gaocheng Pb-Zn-Ag deposit detail exploration,the author's procedures which include inserting various samples to monitor and assess sampling,preparation and assay quality as well as the assay results got,all have meet the QA/QC requirements of NI43-101 issued by Canadian securities.In addition,sample assay data was approved by independent consulting institutions.It has certain reference significance.
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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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".