Investment possibility based models for public–private partnerships in water projects
Bibliographic record
Abstract
One of the key issues that govern the success to invest is creating prospects for the return of investment. However, this is often hampered by a lack of research in determining the region or the area that has the potential for such a project delivery method, and the ability to repay the loan has not been considered. Developing positive cash flow projects depends on the inclination and ability of the customers to pay for the offered services. The aim of this paper is to (i) investigate the effect of Gross National Income (GNI) and the percentage of the population with access to potable water on selection of candidate countries for public–private partnership (PPP) investment in water projects and (ii) model the relationship between (GNI) and the percentage of the population with access to potable water and candidate countries. Four models have been developed to categorize the countries into investment groups. Data used in this paper, as well as the percentage of their respective populations that have access to potable water, were collected from 195 countries. K-means and discriminant analysis techniques have been used to build four investment decision making models. These models have been validated using real data from 40 countries and are helping PPP developers and investors select the region or area that has access to potable water and the ability to repay the loan using GNI.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".