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
Software as a service creates the possibility of composing software applications from web services spread across different application domains. To guarantee certain quality of services of the composite service, one can think of two paths ahead: quality of service negotiation and guarantee prior to service deployment and bindings; or a more speculative and adaptive behavior at runtime. In this position paper we propose a hybrid approach, combining development and runtime information to make the web services adapt to workload variations. The approach combines control theory with performance modeling and is built around a model of the web service. A control loop theory approach is taken to model discovery. The control loop allows for keeping the web service's performance even when the model is not completely known and failure of components of the control loop are likely to happen. The approach is related to robust state estimation. The robustness makes the model insensitive to parameter variations and to uncertainties in the model. With appropriate conditions, the above concept can be extended to the external environments in which the web service has to perform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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