The phrase "attributed at random" is correct and usable in written English.
It can be used when discussing the assignment of characteristics, qualities, or actions to subjects without a specific basis or reason.
Example: "The results of the survey were attributed at random to various demographic groups, leading to inconclusive findings."
Alternatives: "assigned randomly" or "designated at random".
Inserts mapping to several positions on the references with the same "mapping quality" (i.e. number of mismatches and quality of the bases generating the mismatches) were attributed at random to one of them and attributed a "0" mapping quality.
Only membranes with a good pattern of spotting were retained and were attributed at random for the hybridization with the P-cDNA from the tissues.
Reads mapping to several positions on the reference sequence with the same "mapping quality" (i.e. number of mismatches and quality of the bases generating the mismatches) were attributed at random to one of them with a "0" mapping quality.
There was no initial disequilibrium, that is, all alleles were present more than once in the population, and the alleles were attributed at random to each individual for each locus.
A total of 54 focal females and 156 stimulus males and 39 stimulus females were housed outdoors in seventeen aviaries, in which they were attributed at random (aviary size: 3.5 m × 3.5 m × 3 m; about fifteen individuals per aviary).
The intraclass correlation in the random slope model is equal to.36, indicating that 36% of the total variance is attributed at the prefecture level, which is significantly high.
Some of these effects on bw may be attributed to random selection of offspring at the individual time points, but all offspring were weighed at PND4.
In one connective window, satellite link would be blockaded randomly, attributed to random events (energy exhausted or radiation damage) at unknown times.
Deviations are attributed to random fluctuations (see standard deviations).
In a Random Forests ensemble, each tree is built on a bootstrap sample from the original learning sample and, at each test node, K attributes are selected at random among all candidate attributes before determining the best split.
Terror continued to assign roles at random.
