Editorial Announcements and Notifications

Dear authors and readers of the Academic Bulletin of UralNIIproekt,

Continuing our discussion of our collaboration, we would like to share what goes on behind the scenes in the editorial «kitchen.» I am confident that understanding these details will help you interact productively with everyone involved in preparing a manuscript for publication.

It is no secret that the journal’s formal requirements are sometimes perceived as excessive or dictated by obscure considerations. In reality, they are essential for promoting your articles — that is, for effectively bringing your ideas or scientific principles to fruition.

In recent years, in addition to editing, stylistic refinement, and clarification of terminology or figure captions, our editorial team has been preparing manuscripts for machine verification in databases. If the reference list is inconsistently formatted, the DOI is incorrect, or your affiliation does not match the organization listed in your information sheet, algorithms will fail to detect your work. Citation databases such as RSCI, eLibrary, Scopus, Web of Science, and Crossref will not index your paper, and your colleagues will be unable to find it through links. In extreme cases, a dissertation defense committee may disregard such an article, as if it never existed. You have written a paper, but it remains invisible.

This situation makes adherence to formal requirements for scientific articles even more critical.

Today, a new tool has emerged to address this: machine verification of articles through cross-checking against databases. Neural networks help identify incorrect bibliographic information or erroneous dates in an author’s manuscript. They serve as editorial assistants, ensuring that your properly formatted paper is visible to all participants in the scientific process. This way, those interested in your topic—whether in urban planning or architecture — can find your work, cite it, reference it, and apply your research findings.

You can interact with artificial intelligence yourself, even before submitting your article to the editorial office. The only requirement is the skill to formulate precise questions.

Neural networks are powerful tools that respond to the input they receive. Nothing tells the network that a given task is incorrect, unfeasible, or, conversely, meaningful and promising. If the input is inaccurate, the result will match accordingly. And, of course, artificial intelligence cannot generate new knowledge.

The main mistake researchers make today is treating a neural network as a database, like a search engine or a library. It should be used when your manuscript is already complete and you wish to improve it.

Here are examples of questions that enhance the quality of your article and uphold the high standards of our journal. Instead of asking the system to compile a list of articles or data (which it might fabricate), you should request a check on whether your article already exists. Similarly, rather than asking it to write a historical overview, you should ask it to identify logical inconsistencies and contradictions.

We hope to master these new tools for working with text without compromising the quality of scientific research, and we wish you every success. The editorial board is ready to assist you in this endeavor.

Vladimir Gennadievich Veniaminov
Editor-in-Chief Akademicheskie Vestnik UralNIIproekt RAASN