Showing 561 - 580 results of 18,297 for search 'significant ((((((gap decrease) OR (a decrease))) OR (teer decrease))) OR (mean decrease))', query time: 0.65s Refine Results
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    Loss of MALS-1 function suppresses the mitochondrial and axon degeneration phenotypes that are caused by loss of RBM-26 function. by Tamjid A. Chowdhury (14149846)

    Published 2024
    “…For panel B, error bars represent the standard error of the mean (<i>n</i> = 25), and statistical significance was analyzed by one-way ANOVA with a Tukey post hoc test (*<i>p</i> < 0.05 and **<i>p</i> < 0.01). …”
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    Overexpression of MALS-1 reduces mitochondria in the PLM axon and causes axon degeneration and axon overlap defects. by Tamjid A. Chowdhury (14149846)

    Published 2024
    “…For panels D and E, error bars represent the standard error of the proportion. Statistical significance in panel F was analyzed by Student’s <i>t</i> test, ***<i>p</i> < 0.0001 and error bars represent the standard error of mean. …”
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    Group-level narrow- and broad-band spectral changes after hemispherotomy reveal a marked EEG slowing of the isolated cortex, robust across patients. by Michele Angelo Colombo (22446342)

    Published 2025
    “…<b>(E)</b> Following surgery, the PSD became steeper in the disconnected cortex, as indexed by a significant pre- to post-decrease in Spectral Exponent toward more negative values, observed in all patients (i.e., all negative post-pre differences for the disconnected cortex). …”
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    Table 2 - by Rosanna Mary Rooney (17595801)

    Published 2024
    “…Despite a global decrease over the last 30 years, youth crime remains prevalent. …”
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    Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

    Published 2025
    “…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”
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    Sample attribute table. by Mehdi Mourali (10170245)

    Published 2025
    “…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”
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    Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

    Published 2025
    “…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”
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    Sample scenario description. by Mehdi Mourali (10170245)

    Published 2025
    “…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”
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    AMCEs – Pooled across scenarios. by Mehdi Mourali (10170245)

    Published 2025
    “…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”