CONTROL SAMPLES USING FOR QUALITY ASSURANCE AND CONTROL (QA/QC)
Bibliographic record
Abstract
Іn geological exploration, the quality of the data underlying of resource and reserve estimation is critically important. According to various international regulations and current world best practices, Quality Assurance and Quality Control (QA/QC) programs are a necessary part of geological exploration. Quality Assurance (QA) is used to avoid the problems with quality, Quality Control (QC) is aimed at detecting them in case of their occurrence, and together they form the overall Quality System – QA/QC. The article considers the main stages of the history of QA/QC development, which begins in the Middle Ages and continues in our time. Control samples used to control analytical tests have different types and purposes / functions: standards, preparation blanks, coarse blanks, analytical blanks, field duplicates, coarse duplicates, pulp duplicates and umpire laboratory control. Their amount and ratio is the main topic of the research. According to the published data of nine different authors, the recommended control sample amount of each type is different. On average, the amount of control samples of all types is about 20% of the total number of routine samples. In order to highlight the current state of the issue, the authors of the article have analyzed 111 QA/QC programs for 87 ore projects according to the public reports disclosed by the issuers of the Toronto Stock Exchange. So, in practice, the control sample amount does not necessarily reach the recommended 20 % and is slightly more than 16 %. The main conclusions regarding the amount and ratio of control samples can be presented as follows: (1) the general increasing trend in the amount and variety of control samples is observed; (2) among the control sample types, the so-called standards are most often used, and the least used are coarse blanks; (3) the control sample amount and variety also depends on the mineral type; (4) at more advanced exploration stages of the project, the control sample relative number usually increases, but in some cases it may decrease if the results of the previous stages are satisfactory, and the methodology and laboratory are not changed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".