Bibliographic record
Abstract
Determination of reaeration coefficient is an important factor in surface water quality modeling as it determines the efficiency of the Streeter-Phelps model used for predicting dissolved oxygen deficit of any stream. This study compared the efficiency of Atuwara model with ten other reaeration coefficients models by making use of three data sets obtained from river Atuwara during the prevalent wet and dry seasons using composite goodness of fit test which was developed by quantitatively combining statistical and graphical goodness of fit. The eleven tested models were ranked in order of performance. Results show that the four top ranking models were developed through a process that utilized data from multiple streams while models that were developed from data obtained from the test subject alone performed less competitively. The outcome of the study also suggests that the usual practice of selecting the best model based on statistical analysis alone does not necessarily yield the best result and therefore recommended the incorporation of quantitatively analyzed graphs. The paper concludes that selection of the best performing model among existing reaeration coefficient models using the composite goodness of fit may present a cheaper and better alternative to conventional model development approach.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".