#weighting
Live, measured metrics for the hashtag #weighting from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #weighting
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mas.to (Mastodon public tags API) · fetched 2026-10-14 00:46 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mas.to (Mastodon public search API) · fetched 2026-10-14 00:46 UTCLive pulse
measured · mas.to (Mastodon tag timeline) · fetched 2026-10-14 00:46 UTCEverything below is measured over the latest 12 public posts (spanning ~32885 hours).
Posting hours (UTC) — busiest: 16:00
Languages: English (11) · German (1)
Avg boosts / post: 0.4
Top of the latest posts
Scientists Combine Climate Models For More Accurate Projections -- https://phys.org/news/2023-11-scientists-combine-climate-accurate.html <-- shared technical article -- https://www.nature.com/articles/s43247-023-01009-8 <-- shared paper --
There is the difficulty of "evidence" being a category of everything that has been claimed to be evidence. This grouping has included dreams and "spectral evidence". With the category being broad and non-exclusionary, it seems there is effe
#statstab #575 How to create a(n almost) fully Bayesian outcome model with inverse probability weights Thoughts: An interesting way to do IPTW within a Stan/bayesian framework. #brms #stan #iptw #causalinference #weighting #rstats #r #bayes
What “weighting” means
WikipediaThe process of frequency weighting involves emphasizing the contribution of particular aspects of a phenomenon over others to an outcome or result; thereby highlighting those aspects in comparison to others in the analysis. That is, rather than each variable in the data set contributing equally to the final result, some of the data is adjusted to make a greater contribution than others. This is analogous to the practice of adding (extra) weight to one side of a pair of scales in order to favour
“Frequency Weighting (spectral analysis)” on Wikipedia (CC BY-SA) →#weighting across platforms
every network with a public tag surfaceFollow #weighting straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/weighting