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If I may venture a guess, Madam…..

Have you considered the cannabinoid CBG?:pass:

CBG is like the “mother” cannabinoid. As in in, all cannabinoids start out as CBG and then evolve into their final form (thc, cbd, etc). This only applies to cannabinoids, not terpenes.

Although, the terpenes are highest earlier in flower, so you may be getting a bigger % of terpenes as well….

Just a theory, anyway…. I’ve been thinking about this for a year…:crying:

Now CBG does ring a bell.......... :pass:... but I'll have a Google when the kids go.......

All Thoughts gratefully received...:biggrin:... I've been thinking about it for 3 years....:headbang:... and I haven't explained it yet.

I Honestly thought that it might be coming off the pistils because of how I gather it..but @Waira says not the same Trics on the pistils...

Same genetics for 5 historic lines...then 1 parent different...and one cross frogs... and the other doesn't... little mysteries...:biggrin:
 
This is what @Fermented_Fruitz thinks I look like out there walking all the time

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:rofl: :rofl: :rofl: :rofl: :rofl: :rofl: :pass:
 
:pass:

 
Good Morfnoevight All! EO.

I've done Bayesian modeling, as well as other types of computer modeling, and think I understand its limitations. "Garbage in - garbage out" comes to mind. The end results depend sensitively on the input, and in the specific case of Bayesian approaches, the input is often educated guesswork of general relationships between variables, not strong data about them. The tool can be used with care for exploring sensitivity of modeled systems to particular influences, but pretending that accurate future predictions can be generated is in my opinion optimistic. The modified approach described in the paper seems to intend to improve the Bayesian process by using observations to adjust the Bayesian functions, but predictions based on Bayesian models will still be limited by the underlying weakness of applicable data. The very use of the Bayesian approach is admission that data which would allow more precise modeling approaches are not possible due to lack of good data. They too often in my opinion dress up somewhat educated guesses as something more reliable. Basing important decisions on them is risky.

Bayesian modeling discussions on a stoner forum. Who woulda thunk? :crying:
GiGo :rofl: like you I have worked with Bayesian modeling and that is why I thought I knew where they were headed but they took me to the twilight zone.
 
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