Literature — 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 26 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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 the other topics.
Observations
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Content that also appears on other registered channels
Posts published here appear in near-verbatim form on 3 other registered channels. This entry also sits inside a group of 5 channels linked by matching posts, directly or through other channels in the group; two members of the group may share no posts with each other. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
Matching posts — open both and compare (6 of the pairs behind the counts below)
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 7 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.
“Published first” means first in this corpus. We hold 16 comparable posts for this entry, running 12 November 2023 to 15 December 2025. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).
Of the group of 5 channels, the one whose matching posts we hold earliest is @roman_shubat. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_copy, last confirmed 11 November 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
Channels linked by matching posts
This channel is in a group of 5 registered channels linked by matching posts, directly or through other channels in the group. This channel may share no posts with some of the channels listed below, and listed channels may share none with each other. Links are found by comparing text fingerprints across every channel on the register.
Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
10 measurements spanning 99 days, net -10. 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 112–128 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
13 Nov 2026, 01:07
116
+2
4 Nov 2026, 22:29
114
-3
28 Oct 2026, 10:31
117
-3
20 Oct 2026, 13:46
120
+1
1 Oct 2026, 08:00
119
+2
11 Sept 2026, 09:20
117
-4
26 Aug 2026, 20:11
121
-2
13 Aug 2026, 08:25
123
-3
9 Aug 2026, 02:16
126
no change
6 Aug 2026, 10:59
126
first reading
Engagement
19 posts held, back to 12 November 2023 — 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 19 posts for this entry, the most recent from 15 December 2025. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
9s
Average length
9s
Measured directly from 1 video 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
31 reactions across 14 posts, in 2 distinct kinds. The most used accounts for 51.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
16
51.6%
👍
15
48.4%
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 16 of the 19 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 31 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 19 most recent posts we hold, published 12 November 2023 to 15 December 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
📚 رمان آموت
✍️ به قلم ملیکا شاهوردی
📝 خلاصه
یه حماقت! باعث شد یه دختر شونزدهساله پا تو حجلهی مردی بذاره که مثل ببر گرسنه، تشنه دریدن تن اون بود. مردی که بیست سال تبعید اون رو به سایهای سرد و بیرحم تبدیل کرده بود. نوحا، پسر خان روستا که تنها اسمش و آتش کینهاش وحشت به جان همه میانداخت و حالا تشنهی انتقام بود. برای فرار از ازدواج، بکارتش رو پیشکش کرد تا قربانی خون بس و ازدواج اجباری نشه! ولی نمیدونست تو قفسی…
* برشی از رمان لامور به نویسندگی ملیکا شاهوردی *
! پارت گذاری در اپلیکیشن باغ استور!
- میشنوی بهت چی میگم؟! میگم نمیخوامت! نه تورو میخوام نه این عروسی و ازدواجو. شنیدی؟!
بیخیال سر تکان داد و از یخچال شیر را در آورد.
- اوتمیل میخوری؟! رژیم غذاییت چیه لیست کن بده بهم رفتیم لهستان اذیت نشی.
او داشت حرف میزد و روهان حرفهایش را به باد تمسخر گرفته بود و این حرکت آنچنان هیوا رو اذیت کرد که از جایش بلند شد و عصبی به…
دوروز بعد از تولد هجده سالگیم، تو اوج رویای کانادا رفتنم یه حقیقت وحشتناک رو فهمیدم.
"من عروس پیشکش یه مرد سی و شش ساله بودم. "
روهان! کسی که مثل اسمش پاک و زلال نبودو توی تمام سالهای زندگیش تو دنیای زیرزمینی لهستان، تبدیل به یه مافیای سیاه شده بود.
من قربانی یه عهد شدم. عهد اون مرد با پدرم که هجده سال پیش باهم بسته بودن.
موقع به دنیا اومدنم اون روم اسم گذاشت! تو گوشم اذان خــــوند.
اسم من رو گذاشت هیوا، چون من امی…
¬ تخفیف ویژهی باغ استور ¬
از امشب تا 30 اسفند مهلت دارید تمامی رمان های من رو با سی درصد تخفیف استثنایی خریداری کنید.
مارگاریتا:
قیمت قبل تخفیف 70
قیمت بعد تخفیف 49
شوبات:
قیمت قبل تخفیف 75
قیمت بعد تخفیف 52
دقت کنید که بعد اتمام تخفیف افزایش قیمت داریم. پس این فرصت عالی رو از دست ندید و شوبات و مارگاریتا رو با یه قیمت عالی تهیه کنید.
از 27 تا 30 آذر سی درصد تخفیف یلدایی روی تمامی رمان های من داخل اپلیکیشن باغ استور 😍
مارگاریتا
قبل تخفیف: 56
بعد تخفیف: 39
شوبات
قبل تخفیف: 60
بعد تخفیف: 42
از تخفیف جا نمونید که بعد به صورت قطعی افزایش قیمت داریم.
هی میگفتید تخفیف تخفیف
بفرمایید اینم یه تخفیف نامبروان 😍
- برام مهم نیست که ازم متنفری. من مسئول احساسات تو نسبت به خودم نیستم. اما در قبال احساس خودم بهت، تا تهش مسئولم و باید تو هر شرایطی مراقبت باشم دخترم. بهتره هرچه سریع تر قبول کنی که تو مال منی و متعلق به داريوش! حتی اگر خودت اینو نخوای مارگاریتا فرشته!
#مارگاریتا
#نویسنده_ملیکا_شاهوردی
برای مطالعه وارد اپلیکیشن باغ استور بشید 💋
#مارگاریتا
#نویسنده_ملیکا_شاهوردی
سه روز مونده به عروسیم، تو شب تولد چهل سالگیش باهاش آشنا شدم.
داریوش! دوست پسر صاحبکارم که تو آنتالیا به ظاهر خیریه پدر خوندهاش رو اداره میکرد. ولی در باطن، یکی از گنگسترهای خطرناک استانبول بود که سازمان جنسی داشت و همه اون رو به اسم مارگاریتا میشناختن!
مارگاریتا چشمش من رو هدف گرفته بود و تو اون سازمان لعنتیش میخواست! منی که تو روز عروسیم، داماد با زن عقدیش اومد و...
عیارسنج…
از همینجا اعلام میکنم.
منبــــــع رسمی رمان هـــــــــــای:
شوبات
نقاب شیطان
قانون 38
باهور
سوژه
راز بازی ناتمام
آرابسک
داژفوک
شوبات
شامگاه حوا
فقط و فقط و فقط اپلیکیشن بــــــاغ استوره 😍
هرجایی غیر از باغ استور فایل این رمان هارو دیدید بدونید بدون رضایت من و حرامه.
فایلی که سایت های دزدی و افراد دزد گذاشتن فایل ناقصه که خوندش حرامه و شریک شدن تو جرم این افراد دزد.
منبع رسمی رمان های من فقط اپلیکیشن باغ اســـــ…
حورا، رقاص حرفهای مجالس ممنوعه است. دختری که خون افیونِ حوا در رگهایش میجوشد و طعمهی میکائیل میشود.
خون آشامی تبعید شده که در آخرین روز از قمر در عقرب هر سال، پنج دختر باکره به شامگاهش میروند و خودشان را پیشکش او میکنند.
حورا ناخواسته در تله میافتد و به عنوان پنجمین قربانی هم آغوش میکائیل میشود. اما...
#شامگاه_حوا
#نویسنده_ملیکا_شاهوردی
نصب رایگان ios برای آیفون :
https://baghstore.net/app/
نصب رایگان ن…
پرش به پارت های عیارسنج 👆
باقی رمان و پارت های فول هیجانی داخل باغ استور 😍
Showing the 12 most recent of 19 posts we hold for @romann2280. 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.
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 13 November 2026 — this
entry's latest reading, not the date you are reading this.
“محافظ رمان های ملیکا شاهوردی” (@romann2280), 116 subscribers as measured 13 November 2026. Telegram Register, tgregister.com/channel/romann2280.
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.
