Bibliographic record
Abstract
Web services are applications that communicate over open protocols such as HTTP using structured forms of XML such as the Simple Object Access Protocol (SOAP) or Remote Procedure Calls for XML (XML-RPC). The success of web services is largely based on the continuous development of standards that ensure interoperability. Among the many standards developed and widely accepted are: the Web Service Description Language (WSDL), used for describing web services' syntax; and the Universal Description, Discovery and Integration protocol (UDDI), often used as a discovery mechanism for dynamically finding new services. However, there have been fewer efforts to describe the interactions between clients and services. This paper focuses on augmenting web service clients as a means for determining optimal service providers. A system is discussed and analyzed for using the Resource Description Framework (RDF), the Java Expert Systems Shell (JESS), WEKA, and the Web Ontology Language (OWL) to augment web service clients. The clients can collect, report, and analyze data about their experiences with the quality of service (QoS) of web services, as well as their own system context information. The clients are able to parse and use the reported information to dynamically select the best service for their needs, to re-configure themselves to use the new service, and continue operation transparently.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.024 |
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".