INTEROP deliverable DTG 6.2 : Method repository
Bibliographic record
Abstract
This deliverable presents the INTEROP method chunks repository (MCR), its architecture and provided services. It includes the definition of a reusable method chunk, its structure, illustrated with examples of method chunks stored in the repository and guidelines for method chunks definition and characterisation covering tasks TG6.2 and TG6.3 of the work plan of the task group.\nThe main result is the definition of the structure of the method chunk repository emphasizing the link to interoperability. Interoperability is a first-class concept in the structure of the method chunk repository. It not only characterizes method chunks, i.e. procedures to solve interoperability problems, but also interoperability cases, i.e. the presentation of actual problems involving interoperability issues.\nTG 6 has produced three MCR prototypes. Two experiments were undertaken using the Metis system and one using ConceptBase. The task group attended a two-day intense workshop on Metis. As a result, two experiments with Metis as platform for the method chunk repository are under way and reported in this deliverable. One is realizing the structure of the MCR as specified in this report. The other is an alternative approach that serves as a benchmark and is reported in the appendix. The ConceptBase prototype utilizes the metamodel presented in this deliverable.\nWe have analysed three cases involving various aspects of interoperability. One case is about establishing a broker platform for insurance agents, the second about linking the information systems in the public utility sector, and the third case is establishing the relation of the ATHENA Model-Driven Interoperability Framework to the goals of the MCR.\nThe results of the TG6 have been published at the ISD conference 2006 and the ER conference 2006. Copies of the papers are included in the appendix.\nThe report of the example session with the method chunk repository has been shifted towards deliverable TG6.3 (Tutorial of the MCR). This is the more logical place. We want to emphasize that TG6 was not only busy in drafting concepts, exploring the state of the art, and analyzing cases. We are actually experimenting with a prototype and consider this a valuable contribution to the network. As soon as the prototype is stable, knowledge about interoperability solutions can be coded in this repository and can guide designers of interoperable systems by experience knowledge.
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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.250 | 0.240 |
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".