E-Tendering System using Blockchain
Bibliographic record
Abstract
Blockchain technology is set to revolutionize e-tendering by enhancing security, transparency, and cost-effectiveness. Traditional tendering methods, often reliant on paper-based processes or centralized digital systems, are prone to fraud, fake documentation, lack of transparency, and bureaucratic delays. Blockchain-based e-tendering addresses these challenges by eliminating intermediaries, reducing corruption, and fostering trust in the bidding process. By leveraging Distributed Ledger Technology (DLT) and smart contracts, block chain ensures that all transactions are immutably recorded, tamper-proof, and verifiable. This technology mitigates risks associated with data manipulation, unauthorized alterations, and biased decision-making. Automated procurement through smart contracts streamlines workflows, minimizes manual intervention, reduces operational costs, and expedites decision-making. This research explores the fundamentals, benefits, and challenges of blockchain applications in e- tendering, analysing various consensus mechanisms, cryptographic security models, and interoperability issues. By utilizing blockchain- powered e-tendering solutions, organizations can cut costs, minimize fraudulent risks, and ensure a transparent and fair bidding process. The study serves as a foundation for future research on innovative procurement models driven by blockchain and provides insights for government agencies, enterprises, and technology innovators seeking to modernize their procurement systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".