D6.4 – Use Cases System Integration, Deployment & Experimentation V1
Bibliographic record
Abstract
The Deliverable 6.4 of the MOBISPACES project "Use Cases System Integration, Deployment & Experimentation v1" describes the development of the Use Cases of MOBISPACES. For each of the five Use Cases, the document investigates the integration with MOBISPACES systems, the level of deployment and the experimentation done so far. In spite of the short time of development, a preliminary evaluation of the results of each Use Case is presented, in order to give evidence of their potential. In the first months of the project, the main activities held by Use Cases have been the definition of the architecture and the setting of the interfaces between the Use Cases and the general MOBISPACES architecture. Each individual architecture is depicted and described in this Deliverable as an instantiation of the MOBISPACES architecture. The Use Cases have the objective to validate the added value and the effectiveness of MOBISPACES, thus, the Deliverable focuses on the exploitation and adaptation of the different MOBISPACES components to the real situations of the Use Cases. Then, each Use Case has its own requirements, described in Deliverable 6.1. The document aims to confirm, given the preliminary results obtained, the reachability of the requirements and the feasibility of the work configured.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.031 |
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".