Estimating sampling errors for major and trace elements in geological materials using a propagation of variance approach
Bibliographic record
Abstract
Sampling errors produced when geological materials (rocks, soils, tills, drainage sediments) are collected have been estimated empirically using variance decomposition methods or theoretically using Poisson or binomial statistics. Unfortunately, historical distribution-based approaches assume that the element of interest occurs in only one mineral. Although this may be true in some cases, most major oxide and many trace elements reside in more than one mineral in most geological materials. As a result, historical distribution-based approaches do not estimate sampling errors correctly. An alternative theoretical approach to sampling error estimation is proposed that employs both Poisson and hypergeometric statistics, depending on whether the elements of interest reside in rare or common grains. It is intended for use in advance of sampling to ensure that samples in a survey will be colleted in sufficient size to achieve a desired level of sampling precision. This method requires estimates of the proportions, sizes and compositions of the minerals making up the geological material, and thus is based on information readily available from a few (orientation) samples of the material to be sampled. This approach accommodates cases where more than one mineral contains an element of interest. It involves first estimating the sampling error for the minerals present in the geological material. Then, the mineral sampling errors are used to make estimates of the sampling error of all elements within these minerals simultaneously using a simple propagation of variance approach. An EXCEL spreadsheet is provided that undertakes the relevant calculations, and this can be adapted to consider any suite of minerals and elements in geological materials.
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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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".