Bibliographic record
Abstract
Semantic web services are web services with associated semantic descriptions. These descriptions will make it possible for other programs to select, compose and monitor web services at run-time, thereby contributing to the realization of the Semantic Web vision. Researchers have already been active in making the notion of semantic web service more concrete through usage scenaria and technologies that will support their design and use (e.g., [1]). Software Engineering has grappled with the problem of software design for more than three decades. There is welldocumented evidence that designing software manually is a laborious and error-prone task. Semantic descriptions of software (e.g., requirements and design specifications), developed and used at design-time, can facilitate the process of software construction, but they are not panacea. With semantic web services, we seem to be trying to solve the same software development problem, but now these semantic descriptions are used (for selection, composition, monitoring and other purposes) at run-time, automatically. Can we ever hope to see semantic web service technologies and methodologies that actually work? The discussion will focus on this central theme; it will be structured according to the following questions:
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".