A Multi-layered Approach for the Declarative Development of Data Providing Services
Bibliographic record
Abstract
Data Providing Services (DPSs) have the sole purpose of retrieving data from existing sources according to their input parameters while also providing a semantic description of the data they provide using a parametrized view over a domain ontology. A layered model of viewing DPSs is proposed consisting of the data acquisition, syntactic and semantic layers. It is shown that by defining all three layers, a DPS may be generated and managed exclusively by its declarative definition. This will increase the agility and efficiency with which DPSs may be deployed and managed. As a development model, a set of reusable messages are created, these messages are to be semantically annotated using a view over the domain ontology and are syntactically represented such that they may be exported to XML Schema. These messages are used within the DPS definition where their views over the domain ontology are parametrized and the data acquisition layer is defined to acquire data from the source.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".