WW 3.8Beta 莉莉关于 关于拉贝尔 拉贝尔教授是我在「克洛瑞德医师联盟」学习时非常重要的老师。他教给我的不止是知识,更有身为医生应尽的职责——我们作为医生,能做的只有倾尽全力,去救下每一个能救回的人。 关于洛瑟菈 在星炬学院暂住的那段时间,洛瑟菈女士对我非常照顾。后来,我才知道她是新联邦人,在听完我的经历后,她没有说话,只是轻轻地拥抱了一下我。上一次这样做的,还是卡瓦莎女士。不过……她们真的很像,都让人感到温暖、可靠。
🥰82❤8🤨7👍6👏1

Channel
@Team_Gemberry78
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
25,940subscribers
+3,376 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002356846718 |
|---|---|
| Type | Channel |
| Username | @Team_Gemberry78 |
| Description | Wuthering Waves |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 5 November 2026 |
| Measurements held | 60 |
| Confirmed unchanged | 1 time, most recently 5 November 2026 |
| On Telegram | t.me/Team_Gemberry78 |
Gaming — 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 10 September 2026 and assigned it the closest of 31 fixed categories, at 97% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 5 Nov 2026, 19:10 | 25,940 | +4 |
| 4 Nov 2026, 19:30 | 25,936 | +22 |
| 3 Nov 2026, 15:46 | 25,914 | -9 |
| 2 Nov 2026, 14:15 | 25,923 | -8 |
| 1 Nov 2026, 15:14 | 25,931 | -5 |
| 31 Oct 2026, 16:10 | 25,936 | -4 |
| 30 Oct 2026, 18:57 | 25,940 | -5 |
| 29 Oct 2026, 16:17 | 25,945 | -5 |
| 27 Oct 2026, 18:36 | 25,950 | +5 |
| 26 Oct 2026, 15:14 | 25,945 | +7 |
| 25 Oct 2026, 15:49 | 25,938 | +18 |
| 24 Oct 2026, 18:46 | 25,920 | +10 |
| 23 Oct 2026, 16:41 | 25,910 | +101 |
| 22 Oct 2026, 17:20 | 25,809 | +224 |
| 21 Oct 2026, 11:47 | 25,585 | +72 |
| 20 Oct 2026, 08:48 | 25,513 | +200 |
| 19 Oct 2026, 09:18 | 25,313 | +107 |
| 18 Oct 2026, 11:06 | 25,206 | +352 |
| 17 Oct 2026, 11:05 | 24,854 | +99 |
| 16 Oct 2026, 11:16 | 24,755 | first reading |
129 posts held, back to 25 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 132 pages of Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 5 November 2026 |
|---|---|
| Posts held | 129 (25 July 2026 – 5 November 2026) |
| Views total | 815,300 |
| Reactions total | 7,024 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 6 Nov 2026, 08:07 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Lifetime counters from Telegram’s own channel header, read 6 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Measured directly from 4 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.
23,800 reactions across 128 posts, in 61 distinct kinds. The most used accounts for 30.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 💩 | 7,317 | 30.7% | |
| ❤ | 2,515 | 10.6% | |
| 🤡 | 2,024 | 8.50% | |
| 😁 | 1,427 | 6.00% | |
| 🤣 | 1,325 | 5.57% | |
| 🥰 | 1,318 | 5.54% | |
| 👍 | 1,004 | 4.22% | |
| 👎 | 816 | 3.43% | |
| 🔥 | 775 | 3.26% | |
| 🌭 | 711 | 2.99% | |
| 😱 | 621 | 2.61% | |
| 😭 | 541 | 2.27% | |
| 🤯 | 490 | 2.06% | |
| 🎉 | 327 | 1.37% | |
| 🫡 | 315 | 1.32% | |
| 🦄 | 258 | 1.08% | |
| 🤬 | 248 | 1.04% | |
| 🤨 | 228 | 0.958% | |
| 🤩 | 215 | 0.903% | |
| 👏 | 200 | 0.84% | |
| 41 further kinds | 1,125 | 4.73% |
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 128 of the 129 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 23,800 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 129 most recent posts we hold, published 25 July 2026 to 5 November 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’s own ‘edited’ marker: 0 of 47 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
WW 3.8Beta 莉莉关于 关于拉贝尔 拉贝尔教授是我在「克洛瑞德医师联盟」学习时非常重要的老师。他教给我的不止是知识,更有身为医生应尽的职责——我们作为医生,能做的只有倾尽全力,去救下每一个能救回的人。 关于洛瑟菈 在星炬学院暂住的那段时间,洛瑟菈女士对我非常照顾。后来,我才知道她是新联邦人,在听完我的经历后,她没有说话,只是轻轻地拥抱了一下我。上一次这样做的,还是卡瓦莎女士。不过……她们真的很像,都让人感到温暖、可靠。
🥰82❤8🤨7👍6👏1
Photo, posted without a caption
🤬141😭92❤🔥6❤5😁4🍌3
WW 3.8Beta 莉莉关于 关于西格莉卡 返回新联邦前,我在星炬学院停留了一段时间。西格莉卡,是我在那里交到的好朋友。她似乎很懂得把握与人相处的距离,既不会过多追问我的过去,也不会刻意逗我开心。不过开朗的她有时候一个人也会露出落寞的表情。那时候我才意识到这么开朗活泼的女孩,放学后为什么会没有朋友等她呢? 我不想胡乱猜测,但……她可能也失去过重要的人,所以面对我,她才想要紧紧抓住。
😭288❤9🥰8🤯5👎3😱3💔2👏1
Photo, posted without a caption
🤡153🔥9❤5🥰4👍2👏1🗿1😭1
🤣
🤣224❤2😁2🍌1
Photo, posted without a caption
🤡195❤15🤬8😁7🙈7🍌4👎2👏1
哀莫西什,体型类似——守岸人 Amethyst's body type is similar to Shorekeeper.
❤64🤷♀6🌭2🍌2👏2👍1
【Wuthering Wave】4.x I have done my best to find those comparable characters😇
🤡69❤31🖕16👍4🌭2🗿1🤓1
绯雪皮肤展示
🥰30❤4👎2🌭1
NEW UI
😁45👎7🥰6❤3🌭2🥴1
角色的概览新界面/主角形态切换新界面,leaker发一下@全体成员
🤣47😁5🌭1
WW 3.8优化 "连续挑战模式":绯雪这类角色可以在连续挑战中保持二阶段
👏75👍7❤2🌭1
Showing the 12 most recent of 129 posts we hold for @Team_Gemberry78. 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
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 5 November 2026 — this entry's latest reading, not the date you are reading this.
“Team Gemberry78” (@Team_Gemberry78), 25,940 subscribers as measured 5 November 2026. Telegram Register, tgregister.com/channel/Team_Gemberry78.
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.