Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 20 September 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
7 measurements spanning 39 days, net +3. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 719–726 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
15 Sept 2026, 12:37
723
-1
7 Sept 2026, 02:54
724
-1
29 Aug 2026, 13:14
725
+2
16 Aug 2026, 16:56
723
+1
8 Aug 2026, 15:41
722
+2
8 Aug 2026, 02:15
720
no change
7 Aug 2026, 18:24
720
first reading
Engagement
8 posts held, back to 7 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 8 posts for this entry, the most recent from 7 August 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
6m 01s
Average length
2m 00s
Measured directly from 3 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
69 reactions across 8 posts, in 7 distinct kinds. The most used accounts for 37.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
26
37.7%
❤🔥
13
18.8%
custom 5416084417456134513
12
17.4%
🔥
10
14.5%
👏
6
8.70%
🎉
1
1.45%
👍
1
1.45%
Custom emoji. One row above is a Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The count beside it is Telegram’s.
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 8 of the 8 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 69 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 8 most recent posts we hold, published 7 July 2026 to 7 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Telegram Stars
Stars received
1
across the posts below
Posts paid on
1
of 8 we hold a reading for · 13%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @vetfak_kubsau. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 8 most recent posts we hold for this entry, published 7 July 2026 to 7 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Поздравляю с зачислением!
На этой неделе изданы приказы о зачислении на места в пределах квот и основные бюджетные места.
Позади — волнительный период экзаменов, ожидание конкурсных списков, впереди — насыщенная студенческая жизнь в Кубанском ГАУ.
Поздравляю с новым статусом студента одного из ведущих аграрных вузов страны! Желаю набраться сил в заключительные дни лета, чтобы новый учебный год прошёл плодотворно и…
С 1 курса — в дело! 🚀
На кафедре частной зоотехнии завершилась учебная практика студентов первого курса групп ЗМ2501 и ЗМ2502. 🎓 За короткий срок юные зоотехники освоили не одну, а сразу несколько отраслей животноводства – от классического свиноводства до тонкостей пчеловодства. 🐖🐝 Каждый день был насыщен теорией и практикой. 📚
За 8 дней ребята изучили не только тематику научных исследований кафедры и направления дея…
Прошлая неделя в Институте ветеринарной медицины, зоотехнии и биотехнологии выдалась яркой и насыщенной — у нас прошёл долгожданный выпускной 🌟🎉 С гордостью и трепетом сообщаем, что в этом году наш Институт выпустил 204 студента, в числе которых 60 бакалавров, 109 специалистов, 35 магистров. 🎓🥳 Диплом с отличием получили 39 выпускников! 🏆👏 Наши иностранные студенты получили свои заветные дипломы, представляя такие ст…
Дорогой наш абитуриент, совсем скоро ты сделаешь свой осознанный выбор - выбор профессии 💥.
И сегодня мы познакомим тебя с образовательной программой бакалавриата - Зооинжиниринг и цифровизация в животноводстве! На старт, внимание, мотор 🎬!
Сегодня состоялась церемония вручения дипломов аспирантам Кубанского ГАУ. Среди обладателей заветных документов — аспиранты нашего Института👨🎓👩🎓
За годы учебы они показали достойные результаты в науке: их достижения подтверждаются научными публикациями, патентами на разработки, а также победами в престижных конкурсах и получеными грантами🏅🎓
От всей души желаем выпускникам аспирантуры успешно защитить кандидатски…
Преимущества известны, время — выбирать целевое 🎯
Узнать больше можно на нашем сайте по ссылке, ознакомиться с предложениями — на платформе Работа в России.
Целевая квота для тебя!
С Днём семьи, любви и верности ❤️
В мире высоких технологий, где научные открытия совершаются каждый день, а геномы расшифровываются за считанные часы, есть только одна абсолютная константа — семья❤️
Не просто люди с общей фамилией или родством по крови, а те, кто остаётся рядом даже в самые сложные моменты. Те, кто держит нас на плаву, когда опускаются руки от сложных жизненных ситуаций. Те, благодаря кому любая бе…
От черновика до публикации: встреча с кандидатом наук в общежитии №9
2 июля в общежитии №9 состоялся мастер-класс для иностранных обучающихся «Как написать научную статью». Мероприятие провела Фомо Чаппи Ксавьера — наша выпускница, окончившая аспирантуру и успешно защитившая кандидатскую диссертацию. Сегодня она работает в лаборатории нашего университета и на собственном примере показывает: научный путь в России во…
❤🔥4❤1custom 54160844174561345131
Showing the 8 most recent of 8 posts we hold for @vetfak_kubsau. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 15 September 2026 — this
entry's latest reading, not the date you are reading this.
“Институт ветеринарной медицины, зоотехнии и биотехнологий КубГАУ” (@vetfak_kubsau), 723 subscribers as measured 15 September 2026. Telegram Register, tgregister.com/channel/vetfak_kubsau.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.
