@m0l0d0ys_time: Эта легенда не только обои умеет клеить оказывается… #держидверь #боб3 #кава #раст #rust

M0L0D0Y

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Saturday 19 September 2026 19:01:16 GMT

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дмитро :

дед еще может что-то

2026-09-19 19:07:15

10

Gofi :

бля я когда смотрел как подтту музыку дд и боб выдавали а кава на дисциплине застраивался бля было круто вообще

2026-09-22 08:10:17

2

какосик :

бро Чо за музыка?

2026-09-24 17:03:25

0

kykyxa :

👍👍👍

2026-09-19 19:07:24

1

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Female data isn't noisy. Researchers were just measuring it wrong. In this conversation, Dr. Jennifer Garrison breaks down a fundamental flaw in how female biology has been studied — and why it's been hiding in plain sight. For years, researchers have used chronological age to organize and interpret data from female subjects. The problem? Chronological age doesn't account for the enormous variability that exists between women. When you use age of natural menopause as the reference point instead, something remarkable happens. The noise disappears. Sharp, clear biological signatures emerge from data that was previously dismissed as too variable to be useful. And here's what makes this even more striking: researchers were able to go back into large-scale historical datasets that already existed — datasets that weren't originally designed to capture this information — and clean them up simply by changing how the data was organized. The data was never the problem. The framework was. Tune into the full conversation, out on all major streaming platforms.

Female data isn't noisy. Researchers were just measuring it wrong. In this conversation, Dr. Jennifer Garrison breaks down a fundamental flaw in how female biology has been studied — and why it's been hiding in plain sight. For years, researchers have used chronological age to organize and interpret data from female subjects. The problem? Chronological age doesn't account for the enormous variability that exists between women. When you use age of natural menopause as the reference point instead, something remarkable happens. The noise disappears. Sharp, clear biological signatures emerge from data that was previously dismissed as too variable to be useful. And here's what makes this even more striking: researchers were able to go back into large-scale historical datasets that already existed — datasets that weren't originally designed to capture this information — and clean them up simply by changing how the data was organized. The data was never the problem. The framework was. Tune into the full conversation, out on all major streaming platforms.

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