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Some users may have the same level of interest for all spots, whereas other users may have stronger interest for one or several spots among all their interest spots.
Their current interest matrices are analyzed and compared to define the interest difference between user u x and u y as shown in Eq. (4), by averaging their interest difference for all spots.
The same data is modulated for all spots and the arrangement and number of spots is optimized so that at least one spot is in the imager for every receiver position.
By comparing the proportions of the appearance times at a given spot to the total appearance times, the interest degree for all spots of user u i in time t are (F_{u_{i}} (t) = left (f_{u_{i} }^{b_{1}} (t),f_{u_{i} }^{b_{2}} (t),f_{u_{i} }^{b_{3}} (t) cdots,f_{u_{i} }^{b_{p}} (t)right)), where constraints hold as shown in Eqs.
Table S3 shows the least squares means from the statistical analysis of spot density for all spots across treatment groups.
The values of this response index were computed for all spots with background greater than zero (those with zero background, and thus undefined index, were noted and excluded).
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When flagged spots accounted for >5% of all spots, hybridisations were excluded.
Conceptually, a strong response would associate with high AFI for all spot replicates and low variance between replicates, while a weak response would generate the opposite outcome.
We report findings separately for FMV samples only, for non-FMV samples only, and for all spot samples.
Correlation coefficients of Mantel tests of isolation-by-distance were r = 0.297 (P < 0.001) and r = 0.089 (P < 0.001) for all spotted owls and just northern spotted owls, respectively.
For all non-FMV spot samples and for all spot samples (FMV and non-FMV), we estimated the variance attributable to each of three nested components between-child, within-components between-childwithin-childandability (because multiple samples were available from the same day)—using twithin-childdom-intercept models (Marchenko 2006).
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