The EuroGeoSurveys geochemical mapping of agricultural and grazing land soils project (GEMAS) - Evaluation of quality control results of aqua regia extraction analysis.
Bibliographic record
Abstract
Rigorous quality control (QC) is one of the keystones to the success of any regional geochemical mapping programme. For the EuroGeoSurveys (EGS) GEMAS (Geochemical mapping of agricultural and grazing land soils) project 2211 samples (including field duplicates) of agricultural soil (Ap, Ap-horizon, 0-20 cm) and 2118 samples (including field duplicates) from land under permanent grass cover ("grazing land" - Gr, topsoil 0-10 cm) were collected from a large part of Europe, centrally prepared (air dried, sieved to <2 mm, homogenised and split into sub-samples) and randomised prior to being sent out to contract laboratories. QC consisted of (1) collection of a field duplicate at a rate of 1 in 20 field samples, (2) \npreparation of two large project standards ("Ap" and "Gr") for insertion between the routine project samples, (3) preparation of an analytical replicate from each field duplicate and (4) randomisation of all samples prior to analysis. \nHere QC-results covering analysis of 53 chemical elements (Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Ge, Hf, Hg, In, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Pd, Pt, Rb, Re, S, Sb, Sc, \nSe, Sn, Sr, Ta, Te, Th, Ti, Tl, U, V, W, Y, Zn, Zr), following an aqua regia extraction on a 15 g aliquot per sample of both sample materials, are reported. Practical detection limits and precision, as well as the analytical results for the two project standards Ap and Gr are provided for all 53 elements. All analyses were carried out within twenty days at ACME laboratories in Vancouver, Canada. No serious quality problems, other than a few occasional outliers for a number of elements (B, Ca, and Sn) were detected, and the analytical results were accepted after investigating the reasons for these outliers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".