Bibliographic record
Abstract
The Area-based Rating Assessment (AbRA) entails assessing the real property for rating purposes on the basis of the land area only.This study analyzed the implementation of AbRA in Bauchi metropolis, experimentally by testing the helpful factors under both internal and external variables; and subsequently, by testing the harmful factors under the internal and external variables all derived from SWOT analysis, and measuring their effects on the endogenous variable (AbRA).The study derived major themes from SWOT analyses, whose variables under each theme were used as measurement items or sub-themes for the primary data collection.Structural Equation Modelling was used to analyze the impact of the variables on the implementation of AbRA in the study area.It was found that the area assessment required the limited data to conduct the rating assessment, and did not necessarily require periodic revaluation to update the valuation list with the volatilities in value, thus, making AbRA to demand less cost of administration.It was also found that, information on land area can be collected remotely without entry into the individual properties, the process did not defy the residents' privacy, also, the remote data collection could be done using tools like Google Earth/Map which allowed the ease of reconnaissance and faster inspections and measurements, hence, this study recommended the area-based rating assessment (AbRA) for Bauchi metropolis of Nigeria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.989 | 0.995 |
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; both teacher heads agree on what is shown here.
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".