Increasing your Profit Margin through Electronic Procurement
Bibliographic record
Abstract
The advent of the information technology (IT) era has brought internet and all its attendant web activities into the procurement arena. Just as the cyberbuyer is the new generation which has spun off the net, the Electronic Procurement System (EPS) is the engine which will be driving the procurement vehicle down the information highway. A surprising trail-blazer in the driver's seat is the Singapore government which has become the first in the region to implement the much-talked-about modus operandi of the future-the Singapore Electronic Procurement System (SEPS). SEPS was launched in October 1996. SEPS is an IT-enabler for procurement; a tremendously powerful tool that will revolutionise the purchasing climate, reduce overall administrative cost and beef up the organisation's bottom line. It automates and streamlines the laborious routine of the purchasing function, thus freeing up the purchasing professionals to focus on strategic purchases. SEPS has immense on-line potential. And this is just the tip of the iceberg. What's to come will be even more exciting. Get in the driver's seat of this new business machine now. The future is here. Learn to drive, or be left behind on the vast information highway.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.028 |
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".