Dynamic groundwater monitoring networks: a manageable method for reviewing sampling frequency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it