Quality Assessment of Soil Pollution Monitoring: Focus on Representativeness
Bibliographic record
Abstract
<p>Soil monitoring data can be no better than the quality of the monitoring system they stem from. Quality assessment (QA) of soil monitoring requires reliable and comprehensive quality assessment and quality control (QA/QC) schemes including (1) the selection of parameters and measurement quality related to (2) space and (3) time. It can be presented by a synoptic diagram with three axes based on a table with quality criteria. The two major quality parameters are the degrees of resolution (precision) and representativeness (bias), whereas the latter does not yet include parameter selection and soil sampling.<strong> </strong><strong></strong></p>As a result the quality of soil monitoring is usually greatly overestimated. This finding is supported by examples and practical recommendations are given. Since full representativeness for the three aspects of soil monitoring is a fiction in practice, their biases have to be quantified completely, continuously and reliably. The most important challenges are to quantitatively assess and control the representativeness of primary soil sampling and to improve it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".