Virtual Embassy Portal: The Future of Travel
Bibliographic record
Abstract
As the advent of travel continues to increase, tourism continues to expand, becoming the fastest growing economic activity globally. In order to travel internationally, one needs a passport and in many cases a visa is required. Currently, in order to apply for a visa, consumers have to complete and submit an application: online application completion and submission, visit a website to download the application form(s), physically visit an embassy/consulate, or utilize the services of a third party company to submit the application. Thereafter, there is a great chance the consumer might have to appear in person at the consulate/embassy for an in-person interview. There is a need to streamline the visa application process so that it is more efficient for both applicants as well as embassies. This paper aims to fill the gap by introducing the Virtual Embassy concept. This concept promises to bring all embassies to an applicant’s fingertip while making embassies more efficient in the process. The Virtual Embassy enables individuals to complete one application form which can be disseminated virtually to one or more consulate(s) of choice, without requiring extensive travel to embassies, or to manually complete multiple visa application forms.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.021 |
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".