Average Relative Error in Geochemical Determinations: Clarification, Calculation, and a Plea for Consistency
Bibliographic record
Abstract
Research Article| July 01, 2007 Average Relative Error in Geochemical Determinations: Clarification, Calculation, and a Plea for Consistency C.R. Stanley; C.R. Stanley 1Department of Earth and Environmental Science, Acadia University, Wolfville, Nova Scotia, B4P 2R6; e-mail: cliff.stanley@acadiau.ca Search for other works by this author on: GSW Google Scholar D. Lawie D. Lawie 2ioGlobal, Level 3, IBM Building, 1060 Hay Street, West Perth, Western Australia, 6005, Australia; e-mail: dave.lawie@ioglobal.net Search for other works by this author on: GSW Google Scholar Exploration and Mining Geology (2007) 16 (3-4): 267–275. https://doi.org/10.2113/gsemg.16.3-4.267 Article history received: 14 Jul 2006 accepted: 17 May 2007 first online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation C.R. Stanley, D. Lawie; Average Relative Error in Geochemical Determinations: Clarification, Calculation, and a Plea for Consistency. Exploration and Mining Geology 2007;; 16 (3-4): 267–275. doi: https://doi.org/10.2113/gsemg.16.3-4.267 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyExploration and Mining Geology Search Advanced Search Abstract The measurement of error in assays collected as part of a mineral exploration program or mining operation historically has been undertaken in a variety of ways. Different parameters have been used to describe the magnitude of relative error, and each of these parameters is related to the standard measure of relative error, the coefficient of variation. Calculation of the coefficient of variation can be undertaken in a variety of ways; however, only one produces unbiased estimates of measurement error: the root mean square coefficient of variation calculated from the individual coefficients of variation.Thompson and Howarth's error analysis approach has also been used to describe measurement error. However, because this approach utilizes a regression line to describe error, it provides a substantially different measure of error than the root mean square coefficient of variation. Furthermore, because regression is used, Thompson and Howarth's results should only be used for estimating error in individual samples, and not for describing the average error in a data set. As a result, Thompson and Howarth's results should not be used to determine the magnitudes of component errors introduced during geochemical sampling, preparation, and analysis.Finally, the standard error on the coefficient of variation is derived, and it is shown that very poor estimates of relative error are obtained from duplicate data. As a result, geoscientists seeking to determine the average relative error in a data set should use a very large number of duplicate samples to make this estimate, particularly if the average relative error is large. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.206 | 0.575 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".