Record extension for short-gauged water quality parameters using a newly proposed robust version of the line of organic correlation technique
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. In many situations the extension of hydrological or water quality time series at short-gauged stations is required. Ordinary least squares regression (OLS) of any hydrological or water quality variable is a traditional and commonly used record extension technique. However, OLS tends to underestimate the variance in the extended records, which leads to underestimation of high percentiles and overestimation of low percentiles, given that the data is normally distributed. The development of the line of organic correlation (LOC) technique is aimed at correcting this bias. On the other hand, the Kendall-Theil robust line (KTRL) method has been proposed as an analogue of OLS with the advantage of being robust in the presence of outliers. Given that water quality data are characterised by the presence of outliers, positive skewness and non-normal distribution of data, a robust record extension technique is more appropriate. In this paper, four record-extension techniques are described, and their properties are explored. These techniques are OLS, LOC, KTRL and a new technique proposed in this paper, the robust line of organic correlation technique (RLOC). RLOC includes the advantage of the LOC in reducing the bias in estimating the variance, but at the same time it is also robust to the presence of outliers. A Monte Carlo study and empirical experiment were conducted to examine the four techniques for the accuracy and precision of the estimate of statistical moments and over the full range of percentiles. Results of the Monte Carlo study showed that the OLS and KTRL techniques have serious deficiencies as record-extension techniques, while the LOC and RLOC techniques are nearly similar. However, RLOC outperforms OLS, KTRL and LOC when using real water quality records.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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