Application of Receptor Models on Surface Water Quality and Source Apportionment
Bibliographic record
Abstract
Surface water plays an important role in carrying off different water wastes thereby affecting water quality used for different purposes. The Receptor Model (RM) development as a technique in the management of River water was used in this study, in identifying, separating and quantifying the major sources of water wastes flowing into River Musa, Bida, Nigeria. Twelve water variables were used in Principal Component Analysis. The generated variables of loaded components were used as independent variables and the Water Quality Index (WQI) as the dependent variable to estimate the quantity of identified pollutants sources using the Multiple Linear Regression Model (MLR). According to Canadian Council Ministers of Environments Water Quality Index (CCME WQI), the results determined for the five sample stations (Edokota location, Musa bridge location, Bida/Minna location, Ciriko location and Army Barrack location) were 74.4, 72.8, 64.6, 47.6, and 51.6 respectively. Among the five locations, three were investigated to be marginal and the remaining two were fair in rank. The principal component analysis (PCA) was adopted to separate the identified three major waste sources flowing into the river to be agricultural, municipal and industrial wastes. Pollutant levels were determined to be 0.936, 0.457 and 0.104 using RM at a high value of R2 (0.911). Agricultural waste was predicted to be the strongest pollutant contributor in the model, followed by municipal and the least contributor is industrial waste. It is strongly recommended that periodic monitoring and evaluation of the river water quality is carried out within the study area using the receptor model
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".