A checking system for quality control in soil analysis laboratory in Namibia
Bibliographic record
Abstract
Errors due to mistakes in sample handling, labeling, data transcription, and computation may be undetected by normal quality control/quality assurance (QA/QC) procedures. Manual detection of random errors relies on a thorough knowledge of soil properties, which may be lacking in technical staff. This paper describes the development of a checking system that was devised as part of the QA/QC program for a soil analysis laboratory in Namibia. The system was developed by analyzing data from previous measurements on 673 topsoil samples. Expected ranges were defined for 20 measurements and four derived ratios. Electrical conductivity, sand content, pH in water, and the presence of free carbonate were the most common soil characteristics to occur in relationships. Only three significant linear relations were found. They were between organic matter and organic carbon, pH measured in water and in potassium chloride, and between electrical conductivity and extractable sodium. The checking protocol was developed by using the logical functions on a spreadsheet to interpret each analysis in terms of the expected distribution and range and its relationship to other measurements. The output was in the form of comments on probable reliability and potential sources of error that might need further investigation. The checking system should significantly improve QC/QA by identifying random errors that are not caught by operational checks involving calibration standards and reference samples.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".