This MR add a new feature that calculate the scores of each players after the evaluation.
This is interesting for players as it return "the winner" of the game session with an easy and deterministic way.
It looks like that:
There are several calculation methods available:
"T1 with sufficient quality": The score is equal to the "real genetic value of trait 1" multiplies by "the provided penalty if the real genetic value of T2 is below the targeted threshold" multiply "the provided penalty if the individual is not resistant". (I think this the logic used at Institut Agro)
"Product T1 x T2": The score is equal to the "real genetic value of trait 1" multiplies by "real genetic value of trait 2" multiply "the provided penalty if the individual is not resistant" (this is what is used at the University of Tokyo).
Also the scores can be calculated "per submitted individual" (the winner is the player that provided the best overall individual) or averaged across all the submitted individuals for each players.
Finally the "control" are always present among the players.
timflutre
commented
8 months ago
This MR add a new feature that calculate the scores of each players after the evaluation.
This is interesting for players as it return "the winner" of the game session with an easy and deterministic way.
It looks like that:
There are several calculation methods available:
Also the scores can be calculated "per submitted individual" (the winner is the player that provided the best overall individual) or averaged across all the submitted individuals for each players.
Finally the "control" are always present among the players.