u/DeltaSHG

In the paper DNA as nanotechnology: Reassessing Life's Origins through the lens of information by H Gondal

A very interesting argument style is deployed in section O to derive the universal generative capacity ceiling

What appears to be a mere probability equation is inverted into a Hartley style argument

Gondal calculates the universal phase transitions as 4.35x10¹¹⁰ by doing particles x time x reaction rate

He then deploys the 2 bits per nucleotide and basically deploys a capacity bound utilizing this formula

n = log T / log M

It yields about 184 base pairs or 368 bits as the Universal generation limit

This is actually independent of biology entirely and appears to be an upper bound of information generation in finite systems utilizing the universe as the finite system

Log 4 is H max aswell

The real interesting bit happens where a selection bias table appears to show corrected per position fidelity across the universal trial space with a curve scaling from 0.25 to 0.99995

n = log T/ - log p

This is interesting as it's directly recoverable from Shannon's formula by substituting S/N = 1-p/p

Appears to be information self reprisal

The variable P serves as a universal plugin for any conceivable model - showing to reach the minimal genome you need information specificity of 99.995% regardless of what the process is

This scaling is compared to the minimal genome by the Venter institute which is around ~1 million bits

It's not an Improbability argument but a capacity argument thus standard rebuttals such as more time or matter doesn't work as it's already been all utilized

Despite that, the table at the end showing multiple abiogenesis models hides the issue of logarithmic compression stating that you can vary trials by alot and the base pair or information limit barely moves

The simplest implication being that the universe does not have the capacity to generate life by random processes and wtv process did help serves as a information bias so strong that it's indistinguishable from pre loaded specificity

I'd appreciate some nuanced takes as this has been boggling my mind given it appears to converge with other limit theorems such as Berkenstein Bound etc

https://www.researchgate.net/publication/395581588_DNA_as_Nanotechnology_Reassessing_Life's_Origin_Through_the_Lens_of_Information_and_Genomic_Intelligence

Calculate the universe's noiseless channel capacity in bits by multiplying total particles 10^80, the reaction rate 10^13 and the universe's age being 4.35x10¹¹⁰ secs

The maximum trial or phase transition number of the universe is ~10¹¹⁰

Now ask - what is the longest string sequence the universe can transmit? Assume 4 letter code is DNA

N = log T/ log M

You're doing information theory math and establishing the Logarithmic relationship between matter and information

The universes maximum channel bit capacity by random is around 184 base pairs or 368 bits

The minimal lab synthetic smallest genome is around 1 Million bits - all known self replicating life exists in this ballpark and can't be compressed further due to dependencies of enzymatic replication etc

Recall Kolmogrov complexity - seeing minimal genome JVC Syn 3A - Double check with Berkenstein and Landauer

Simply put

The universe contains objects that it CANNOT generate

It is tuned in a way to support life but never be able to make it

Logarithmic compression and capacity nuance - this is resources exhaustion

Information precedes matter

Before responding please articulate your thoughts to Incorporate what a capacity argument is and how it's different from probability

Also Shannon Hartley cannot be debated in information theory they are the most attested math

The same math running your computer or phone

This is a basic research space exhaustion bound

Basic information theory nothing controversial

The implications is what solicits the bizzare knee jerk reactions in the comments

Also I am very aware Reddit is not the place to expect expert level nuanced conversations - this post is not to convince - but to notify

Scaling factor

P (Corrected Probability)

-log (P)

N = log T/ - log p

Vary p value between 0 & 1 - see what amount of P yields the minimal genome

Original argument is from an academic paper Section O by H Gondal - it's disguised Shannon as probability

https://datasetcatalog.nlm.nih.gov/dataset?q=0001356871

https://data.mendeley.com/datasets/htdx6rznjg/6

Have two Thailand made F-91Ws that are accurate to the atomic clock with 0 second +-

All my other casios or seikos are being destroyed by this good old legend. A bit taken aback how after 2 months it's not lost or gained anything at all.

One was a special pink casio from Amazon and the other the green/black version from Walmart