Bibliographic record
Abstract
Traditionally web applications have required an internet con-nection in order to work with data. Browsers have lacked any mechanisms to allow web applications to operate offline with a set of data to provide constant access to applica-tions. Recently, through browser plug-ins such as Google Gears, browsers have gained the ability to persist data for offline use. However, until now it’s been difficult for a web developer using these plug-ins to manage persisting data both locally for offline use and in the internet cloud due to: synchronization requirements, managing throughput and la-tency to the cloud, and making it work within the confines of a standards-compliant web browser. Historically in non-browser environments, programming language environments have offered automated object persistence to shield the de-veloper from these complexities. In our research we have cre-ated a framework which introduces automated persistence of data objects for JavaScript utilizing the internet. Un-like traditional object persistence solutions, ours relies only on existing or forthcoming internet standards and does not rely upon specific runtime mechanisms such as OS or in-terpreter/compiler support. A new design was required in order to be suitable to the internet’s unique characteristics of varying connection quality and a browser’s specific restric-tions. We validate our approach using benchmarks which show that our framework can handle thousands of data ob-jects automatically, reducing the amount of work needed by developers to support offline Web applications.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".