Modeling and Simulation of a Decision Support System for Population Census in Nigerian
Bibliographic record
Abstract
In recent times, Nigeria had made series of attempt to know the population dynamics of her people but the results have always been associated with incompleteness and inaccuracy. This study presents the modeling and simulation of the decision support system for population census in Nigeria. A structural framework to implement an accurate and complete population census is presented with analytical models for the classification and tabulation of population parameters such as relation to head of household, sex, age, disability, home local government area, home place, address, literacy, educational qualification, work status, type of employment, sector of employment, marital status, statistics of marriage were formulated. The analytical model was prototyped using the decision support systems approach. The Microsoft visual basic programming language and embedding Microsoft excel where used. A case study of Ilutitun community was carried out to demonstrate the practical implementation of the designed DSS and the result suggested the population structures of the community.
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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 | 0.001 | 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 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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".