Showing 1 - 20 results of 457 for search '(( same ((linear decrease) OR (mean decrease)) ) OR ( ai ((large decrease) OR (marked decrease)) ))', query time: 0.46s Refine Results
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    Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

    Published 2025
    “…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”
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    <b>When more isn’t better: Sperm competition decreases fertilization success and motile sperm in two sea urchin species</b> by Luisa Kumpitsch (20874095)

    Published 2025
    “…</p><p dir="ltr">Description of files:</p><p dir="ltr">fertility_data_cleaned.csv: data on <i>Dendraster excentricus</i> fertilization success means for non-competitive- and sperm competition treatment; used for linear mixed-effects models and basic statistics</p><p dir="ltr">fertility_data_cleaned.xlsx : see above </p><p dir="ltr">Fertility_FHL_means.csv: used for plotting and calculate basic statistics </p><p dir="ltr">Fertility_FHL_means.xlsx : see above </p><p dir="ltr">fertility_vigo_cleaned.csv: data on <i>Paracentrotus lividus</i> fertilization success means for non-competitive- and sperm competition treatment; used for linear mixed-effects models and basic statistics , and to filter for 1500 and 7500 sperm/µl which was used for further analysis </p><p dir="ltr">fertility_vigo_cleaned.xlsx: see above </p><p dir="ltr">Fertility_Vigo_means.csv: calculate statistics </p><p dir="ltr">Fertility_Vigo_means.xlsx: see above </p><p dir="ltr">FHL_FandF2.txt: look at differences between post 2h- and 24h fertilization success in <i>D. excentricus </i></p><p dir="ltr">motility_data_cleaned.csv : perm motility data on <i>D. excentricus</i>, used for linear-mixed effects models and basic statistics </p><p dir="ltr">motility_data_cleaned.xlsx: see above </p><p dir="ltr">Motility_Fertility_FHL_Means.csv: motility and fertilization data on <i>D. excentricus</i>, used to check if motility parameters affected fertilization success </p><p dir="ltr">Motility_Fertility_FHL_Means.xlsx: see above </p><p dir="ltr">Motility_Fertility_Vigo_Means.xlsx: : motility and fertilization data on <i>P. lividus</i>, used to check if motility parameters affected fertilization success </p><p dir="ltr">Motility_Fertility_Vigo_Means.csv: see above </p><p dir="ltr">Motility_Urchins.csv: used for plotting motility data in both sea urchin species </p><p dir="ltr">motility_vigo_cleaned.csv: sperm motility data on <i>P. lividus</i> </p><p dir="ltr">Size_Correlation_F.csv: test sizes and fertlilization success of both sea urchin species, to check if test size affects fertilzation success </p><p dir="ltr">Size_Correlation_M.csv: test sizes and fertlilization success of both sea urchin species, to check if test size affects sperm motility</p><p dir="ltr">Size_Correlation_M.xlsx: see above </p><p><br></p><p dir="ltr"><br></p><p><br></p>…”
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    Participants’ Mean Blood Glucose. by Giti Azim (20940548)

    Published 2025
    “…Moreover, multivariate analysis using multiple linear regression indicates the same result; however, the results of marital status and gender are not significant with BG level and results of education levels, salt intake and any type of physical activity are not significant with TC levels. …”
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    Participants’ Mean Total Cholesterol. by Giti Azim (20940548)

    Published 2025
    “…Moreover, multivariate analysis using multiple linear regression indicates the same result; however, the results of marital status and gender are not significant with BG level and results of education levels, salt intake and any type of physical activity are not significant with TC levels. …”
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    K-means++ clustering algorithm. by Zhen Zhao (159931)

    Published 2025
    “…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”
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