Science — 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 27 September 2026 and assigned it the closest of 31 fixed categories, at 36% 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 +13. 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 88–106 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
13 Sept 2026, 20:38
104
+1
5 Sept 2026, 19:20
103
+2
21 Aug 2026, 09:15
101
+4
13 Aug 2026, 11:24
97
+7
9 Aug 2026, 14:18
90
no change
7 Aug 2026, 05:04
90
-1
6 Aug 2026, 05:02
91
first reading
Engagement
18 posts held, back to 25 December 2025 — 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 18 posts for this entry, the most recent from 21 July 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
16m 39s
Average length
8m 20s
Measured directly from 2 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
34 reactions across 10 posts, in 4 distinct kinds. The most used accounts for 47.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
16
47.1%
❤
9
26.5%
👏
8
23.5%
🔥
1
2.94%
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 11 of the 18 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 34 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 18 most recent posts we hold, published 25 December 2025 to 21 July 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.
📢 معرفی نرمافزار AZ Validation Tool
📈اعتبارسنجی خروجی مدلها و مقایسه آنها با دادههای مشاهدهای، یکی از مراحل کلیدی در بسیاری از پژوهشهای علمی است. این فرآیند معمولاً نیازمند محاسبه چندین شاخص آماری بوده و میتواند زمانبر باشد.
به همین منظور، نرمافزار AZ Validation Tool طراحی و توسعه داده شده است تا فرآیند اعتبارسنجی را سریع، دقیق و ساده کرده و تنها با چند کلیک، محاسبات موردنیاز انجام شود.
🔹 قابلیتهای نرما…
✍️علی زارعی (دانشجوی دکتری اقلیم شناسی در دانشگاه تهران)
📊 اعتبارسنجی چیست و چرا اهمیت دارد؟
🌍 یکی از مهمترین مراحل در پژوهشهایی که در آنها از مدلهای اقلیمی استفاده میشود، اعتبارسنجی (Validation) است. در این مرحله، خروجی مدلها با دادههای مرجع مقایسه میشود تا میزان دقت و قابلیت اعتماد آنها مشخص شود. اعتبارسنجی به ما کمک میکند تا مناسبترین مدل را برای مطالعات اقلیمی و پیشآگاهی از تغییرات آینده انتخاب کنیم…
✍علی زارعی (دانشجوی دکتری اقلیم شناسی در دانشگاه تهران)
دمای سطح زمین (Land Surface Temperature (LST)) یکی از مهمترین متغیرهایی است که استفاده فراوانی در مطالعات محیطی و شهری دارد. این متغیر بیانگر دمای سطوح مختلف از جمله پوششهای گیاهی، خاک، آسفالت، بتن، بام ساختمانها و سایر عوارض سطحی است و از طریق سنجندههای حرارتی ماهوارهای اندازهگیری میشود.
به دلیل نقش اساسی LST در تبادل انرژی میان سطح زمین و جو، این شاخص …
🎥 آموزش استخراج داده از ClimApp
با توجه به بازخورد دریافتی از مخاطبان، ویدیویی آموزشی آماده شده است که در آن نحوه استخراج و دانلود دادهها از ClimApp را قدمبهقدم توضیح میدهد.
🚨لطفا توجه داشته باشید که برای ورود به اپلیکیشن، نیاز است از روش های گذر از تحریم استفاده نمایید.🚨
لینک دسترسی :
https://alizarei.users.earthengine.app/view/climapp
Follow us 👇
https://t.me/GeoHelperData
✍علی زارعی (دانشجوی دکتری اقلیم شناسی در دانشگاه تهران)
🌡️ جزیره گرمایی شهری (Urban Heat Island - UHI) پدیدهای است که در آن مناطق شهری نسبت به نواحی اطراف دمای بیشتری دارند. این وضعیت به طور کلی به دلیل گسترش سطوح مصنوعی، ساختمانها، آسفالت و کاهش پوشش گیاهی ایجاد میشود. شاخص UHI (سطحی) از اختلاف دمای سطح زمین (LST) بین مناطق شهری و غیرشهری (پیرامون) محاسبه میشود؛ مقادیر مثبت بیانگر جزیره گرمایی و مقادیر منفی نشا…
✍️ علی زارعی (پژوهشگر دکتری اقلیم شناسی دانشگاه تهران)
🌎شاخص NDWI (Normalized Difference Water Index) یکی از شاخصهای پرکاربرد در سنجش از دور است که برای شناسایی و پایش پهنههای آبی و مناطق دارای رطوبت مورد استفاده قرار میگیرد. این شاخص با بهرهگیری از بازتاب طیفی سطح زمین، امکان تفکیک آب از سایر عوارض مانند پوشش گیاهی، اراضی خشک و مناطق ساختهشده را فراهم میکند. مقادیر NDWI در بازه 1- تا 1+ قرار دارند؛ بهطوریک…
✍️ علی زارعی
🌍پیشآگاهی (Projection)، برآوردی از وضعیت آینده آب و هوا است که بر اساس مدلهای عددی اقلیمی و واداشتهای مختلف انتشار گازهای گلخانهای ساخته میشود.
📈نمودارهای ارائه شده مقادیر دمای هوا شامل دمای کمینه، میانگین و بیشینه را تا پایان قرن نشان میدهند که برای این منظور از مدل ACCESS-CM2 مجموعه دادههای NASA GDDP-CMIP6 و دو سناریوی اقلیمی SSP2-4.5 و SSP5-8.5 استفاده شده است. برای کاهش عدم قطعیت ذاتی مدلهای…
✍️علی زارعی
🌿شاخص NDVI (Normalized Difference Vegetation Index) یکی از پرکاربردترین شاخصهای سنجشازدور برای پایش پوشش گیاهی است و مقادیر آن بین ۱- تا ۱+ قرار دارد، که مقادیر بالاتر نشاندهنده پوشش گیاهی سالم و متراکم هستند. برای محاسبه NDVI با تصاویر لندست که تفکیک مکانی مناسبی دارند، استفاده از یک نسل خاص از این ماهواره، مانند Landsat 8، برای دورههای بلندمدت امکانپذیر نیست، زیرا این نسل تنها بخش محدودی از بازه…
🌧نقشه میانگین 40 ساله (2025-1986) مجموع بارش سالانه کشور ایران (CHIRPS)
Follow us 👇
Telegram : https://t.me/GeoHelperData
👍2
Showing the 12 most recent of 18 posts we hold for @GeoHelperData. 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
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
Mentions
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
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 September 2026 — this
entry's latest reading, not the date you are reading this.
“GeoHelper” (@GeoHelperData), 104 subscribers as measured 13 September 2026. Telegram Register, tgregister.com/channel/GeoHelperData.
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.