Dynamic groundwater monitoring networks: a manageable method for reviewing sampling frequency
Bibliographic record
Abstract
Optimization of a water quality network through a change in sampling frequency is the only way to increase cost-efficiency without any reduction in the robustness of the data. Existing techniques define optimal sampling frequency based on analysis of historical data from the monitoring network under investigation. Their application to a large network comprised of many sites and many monitored parameters is both technical and challenging. This paper presents a simple non-parametric method for reviewing sampling frequency that is consistent with highly censored environmental data and oriented towards reduction of sampling frequency as a cost-saving measure. Based on simple descriptive statistics, the method is applicable to large networks with long time series and many monitored parameters. The method also provides metrics for interpretation of newly collected data, which enables identification of sites for which a future change in sampling frequency may be necessary, ensuring that the monitoring network is both current and adaptive. Application of this method to the New Zealand National Groundwater Monitoring Programme indicates that reduction of sampling frequency at any site would result in a significant loss of information. This paper also discusses the potential for reducing analysis frequency as an alternative to reduction of sampling frequency.
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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 | 0.014 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".