Service Selection Based on Customer Rating of Quality of Service Attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Selecting the optimal service from a set of functionally equivalent services is non-trivial. Previous research has addressed this issue making use of Quality of Service (QoS) attributes of the candidate services. In doing this, researchers have however assumed that the customers' preference of the various QoS attributes varies linearly with the actual attribute values. In this work, we put forward a technique that overcomes this restriction and compares functionally equivalent services on the basis of the customers' perception of the QoS attributes rather than the actual attribute values. We utilize the 'mid-level splitting' method to track the customer's preference vis-a-vis the actual attribute values. Further, we utilize the 'Hypothetical Equivalents and In equivalents Method' to assign weights, reflecting the importance, to the attributes on the basis of the customer preference. The whole procedure is demonstrated using a simple running example.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it