New Statistical Test to Determine the Best Model for Forecasting Water Discharge Data: Tigris and Euphrates Rivers in Iraq as a Study Case
Bibliographic record
Abstract
In this research, the time series were analysed for four gauges (Mosul, Baghdad, Kut, and Husayabah) using autoregressive (AR) models with constant and periodic autoregressive coefficients. It was found that the best model for Mosul, Baghdad, and Husaybah gauges is AR (2) with periodic autoregressive coefficients, while the best model for the Kut gauge was AR (2) with constant autoregressive coefficients. The test was also suggested to determine the most appropriate model based on the values of autocorrelation of residuals (independent normal variable) and it was compared with the drawings of correlograms of autocorrelation of residuals rk(ξ) and with two tests: the AIC test and the portmanteau lack test. It was concluded that the suggested test was more accurate and more reliable.
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How this classification was reachedexpand
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".