OSSES: An Online System for Studies on Evaluation of Systems
Bibliographic record
Abstract
In recent years, due to exponential increase in the number of internet users, finding the appropriate information is difficult.Web crawlers represent a significant component in Web search engines.This contribution describes a Web based distributed system designed as a 3-tier architecture consisting of: the presentation layer, the business logic layer and the data persistence layer.The main educational benefit of our system is to provide a reference tool that has an interactive database to encourage evaluations of systems that fulfill certain methodological requirements.The synopsis of studies collected can be used as a basis of a searchable online database that provides an overview of the state-of-the-art to the scientific community and encourages other scientists to evaluate their own system.It will also help students identify pitfalls in the planning process as well as in the analysis of collected data and also identify omissions in the state-of-the-art in future.The collaborative nature of our tool enables sharing information among research students providing them a larger view of the state-of-the-art.The architecture includes RSS Feed Management, Paper Subscription, Smart URL Analysis and Document Downloading.The RSS Feed Management allows a user to manage a set of Web feed formats that will publish most recent papers.As soon as a paper is published via RSS Feed, the paper subscription module automatically creates metadata.Upon receiving the document link, the Document Downloading module copies the document to a local repository.The system has been tested and evaluated showing good performance.
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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.026 |
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".