Dynamics of a Decision Support System for Online Tendering
Bibliographic record
Abstract
In other to maximize the full potentials of information technology, both private and public establishments have been increasingly exploring and migrating manual processes into automated processes and the tendering system is not left out in these innovative migrations. This project work is aimed at developing an enhanced decision support system for online tendering which has helped to speed up decision making processes that improve the efficiency of the online tendering process. The specific objectives of this research work are to; Examine the current decision support system for the online tendering, design an enhanced decision support model for commercial bid evaluation in online tendering, Implement the enhanced decision support model for the online tendering and to evaluate the developed enhanced decision support model for the online tendering. The Object-Oriented Analysis and Design method has been used in the development of this project work with the unified modeling language (UML) diagrams used to explain the workability of the various objects of the system. All the modules of the developed system have worked according to specifications and design, and the system performed excellently well. The system has enhanced significantly the time taken to analyze and make decision cutting down cost of printing. Keywords: Decision Analysis, Data Integrity, Evaluation Model, Efficiency Levels, Technological Improvement, Managerial Processes
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".