Spectral analysis in determining water quality sampling intervals
Bibliographic record
Abstract
ABSTRACT To make water quality series more representative, real-time monitoring techniques are developed. However, these techniques have obstacles in their use, such as high costs and difficulties in equipment installation, maintenance, and calibration. One alternative is near-real time water quality monitoring (NRTWQM), with sampling done less frequently than daily. The study objective was to evaluate, through spectral analysis, the water quality sampling frequency representativity for different catchments. For this purpose, a historical series of real time water quality monitoring stations were used in Brazil, Canada, and the USA. These series were submitted to spectral analysis to identify the denser frequencies and their representativeness across the series. To obtain the sampling intervals, the Nyquist-Shannon theorem was applied. Weekly intervals accounted for 65% of cumulative frequencies for the three verified parameters, and the sampling intervals obtained by means of the characteristic frequencies were shown to be executable in the NRTWQM models for up to the 90% of cumulative frequency. For cumulative frequency above 90%, the intervals approach the daily values.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".