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After injection, it is important to sample over time and over a large enough sampling grid to capture pH breakthrough.
One way to provide comprehensive statistical data is Monte Carlo simulation with a large enough sampling scale [21], but the computation time is too long.
A large enough sampling grid over time ensures that the MMV plan can capture the CO2 leak and its growth, thus avoiding false positive/negative results from single point measurements.
In general, only advanced machine learning and regression techniques are able to benefit from more complex covariates, such as per-RB measurements, provided that a large enough sampling base is available (which was not the case for the 2RB + regressor).
In both cases "correlates" implies large enough sampling to ensure statistical relevance.
The first sampling point in season 1 was 7 days after flowering (DAF) and then once the berries were large enough, sampling was conducted 3 times per week.
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In regions with small populations, over-sampling is done to ensure that there is a large enough sample to be representative, while under-sampling is done in regions with large populations as a means of saving cost.
It's probably not a large enough sample.
That's just not a large enough sample size to properly evaluate the entire league.
Some might not have a large enough sample to find a small but statistically significant effect.
We probably do not have a large enough sample size to say much.
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