G25 ancient model for Stears

[Jana]

Scaled? I would also like to know the most fit model for me.

Yeah. It's very hard job, you need to run entire spreadsheet (all ancients available on G25) and than assign what you get to culture that makes sense for it. 0 penaliation btw.
For example, if he scores Avar_Szolad I would move it to Balto-Slavic, if he scored Bulgarian_IA I classed it as Old_Balkan etc.

Very delicate and hard job. I did the same for me, my results are in my sig. Before that I always had horrible fits and could never get any close distances.

[Defcon2]

Yeah. It's very hard job, you need to run entire spreadsheet (all ancients available on G25) and than assign what you get to culture that makes sense for it.
For example, if he scores Avar_Szolad I would move it to Balto-Slavic, if he scored Bulgarian_IA I classed it as Old_Balkan etc.

Very delicate and hard job. I did the same for me, my results are in my sig. Before that I always had horrible fits and could never get any close distances.

I like that idea

[Jana]

I like that idea

Also if you get samples which you don't know/aren't sure what they are, you must run them compared to moderns, so you will be able to identify the culture by the fact to which modern pops they are closest (both distance and modeling)

[gixajo]

Vahaduo custom G25 Calculators, ancients spreadsheet (individual samples not averages)

In unscaled G25 Ancient Individual Samples, official datasheet, I get a very close distance too, but with a longer list of results, without "customize" it.

Spoiler!

Target: gixajo
Distance: 0.2463% / 0.00246336
15.6 Bell_Beaker_NLD:I4068
12.8 Iberia_Northeast_c.6-8CE_ES:I7673
11.6 Iberia_Southeast_MLN:I7600
8.2 DEU_MA:AED_106
5.4 FRA_BA:QUIN58
5.4 Iberia_Southeast_MLN:I7594
5.0 Iberia_Northeast_c.6CE_PL:I12034
4.8 HUN_Starcevo_N:I1876
3.6 Iberia_Menorca_LBA:I3315
3.0 DEU_LBK_N:I2008
2.8 RUS_Saltovo-Mayaki_low_resA190
2.6 DEU_MA:STR_316
2.6 England_CA_EBA:I5373
2.6 ITA_Sardinia_Nuragic:I10365
1.8 FRA_IA:NOR3-15
1.8 Iberia_Northeast_RomP:I8339
1.6 Canary_Islands_Guanche:gun005
1.4 MAR_Taforalt:TAF010
1.4 RUS_Afanasievo:I6711
1.4 SWE_Viking_Age_Sigtuna:vik_gtm127
1.2 Canary_Islands_Guanche:gun008
1.2 CHE_FN_steppe:MX304
0.8 ITA_Collegno_MA:CL121
0.6 ZAF_2100BP:I9028
0.4 PAK_Katelai_IA:I12472
0.2 Bell_Beaker_CHE:I5759
0.2 Levant_ISR_Ashkelon_LBA:ASH33

[Ion Basescul]

I ran entire spreadsheet without removing any samples, and that's why this model is so tight. I don't believe in any models which handpick samples they like and force algorithm to chose them.
He won't get any better model than mine

It's only natural to get a very close fit with a lot of samples. But that doesn't indicate that some of those samples who result in the overfit contributed any real ancestry. For example in your case, it's very unlikely that you have any real North Caucasian ancestry. That's probably limited only to bordering regions in Russia and Ukraine.

[Jana]

In unscaled G25 Ancient Individual Samples, official datasheet, I get a very close distance too, but with a longer list of results, without "customize" it.

Spoiler!

Target: gixajo
Distance: 0.2463% / 0.00246336
15.6 Bell_Beaker_NLD:I4068
12.8 Iberia_Northeast_c.6-8CE_ES:I7673
11.6 Iberia_Southeast_MLN:I7600
8.2 DEU_MA:AED_106
5.4 FRA_BA:QUIN58
5.4 Iberia_Southeast_MLN:I7594
5.0 Iberia_Northeast_c.6CE_PL:I12034
4.8 HUN_Starcevo_N:I1876
3.6 Iberia_Menorca_LBA:I3315
3.0 DEU_LBK_N:I2008
2.8 RUS_Saltovo-Mayaki_low_resA190
2.6 DEU_MA:STR_316
2.6 England_CA_EBA:I5373
2.6 ITA_Sardinia_Nuragic:I10365
1.8 FRA_IA:NOR3-15
1.8 Iberia_Northeast_RomP:I8339
1.6 Canary_Islands_Guanche:gun005
1.4 MAR_Taforalt:TAF010
1.4 RUS_Afanasievo:I6711
1.4 SWE_Viking_Age_Sigtuna:vik_gtm127
1.2 Canary_Islands_Guanche:gun008
1.2 CHE_FN_steppe:MX304
0.8 ITA_Collegno_MA:CL121
0.6 ZAF_2100BP:I9028
0.4 PAK_Katelai_IA:I12472
0.2 Bell_Beaker_CHE:I5759
0.2 Levant_ISR_Ashkelon_LBA:ASH33

This is it. He and me got the same. Now you need to assign names to every sample based on logic and how they plot genetically, and than add all that togheder.
For example all old Iberian you give the same name and merge them togheder, all Germanic like too, all MENA like also etc.

Use your intuition and history/genetics knowledge.

[Jana]

It's only natural to get a very close fit with a lot of samples. But that doesn't indicate that some of those samples who result in the overfit contributed any real ancestry. For example in your case, it's very unlikely that you have any real North Caucasian ancestry. That's probably limited only to bordering regions in Russia and Ukraine.

And you know that how? My grandmother was not Croatian and she had very mixed and complex ancestry that I never learned completely about.
Since her ancestors lived in Budapest before moving to Vojvodina, they could have been mixed with anything, all kinds of people lived in that city.

I have high Kavkaz affinity on every calculator, so yeah. If I remove Kavkaz part my fits worsens considerably.

[17571imre]

This is it. He and me got the same. Now you need to assign names to every sample based on logic and how they plot genetically, and than add all that togheder.
For example all old Iberian you give the same name and merge them togheder, all Germanic like too, all MENA like also etc.

Use your intuition and history/genetics knowledge.

but what if the sample is mixed? and it has a mix of Germanic and slavic? where do you put it then

[Jana]

but what if the sample is mixed? and it has a mix of Germanic and slavic? where do you put it then

I didn't have such problems on 0 pen, it usually gives clean results compared to 0.25X (for example it gives me very med and very baltic samples with no in-betweeners)
If it's mixed you can give it modern name.

I actually had one such case, which was Germanic/Celtic mix and that's why I named it German, not Germanic. It wasn't clean but very modern south German like.
While Stears scored pure Germanic samples, thus his NW part is called Germanic and not German.

Hop you catch the drift

[gixajo]

This is it. He and me got the same. Now you need to assign names to every sample based on logic and how they plot genetically, and than add all that togheder.
For example all old Iberian you give the same name and merge them togheder, all Germanic like too, all MENA like also etc.

Use your intuition and history/genetics knowledge.

I read about a similar method in Anthrogenica.

So now I must group my list of results under a common denomination with some historic sense (for example all Iberian peninsula results as Celtiberian or anything similar, or Guanche and Mar_Taforalt together under another denomination), and put ":" , so that they add up and give higher percentages in results.

Thanks.



Thanks.