Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…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. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

Ethnic identity as a moderator among APIDA students. by Ronna Bañada (19233063)

The correlogram. by Bingtao Su (4740009)

“…Results demonstrated that groups with a lower prevalence of diabetes showed lower dietary EFP, while the dietary EFP and GHG emissions peaked among the prediabetes group with a prevalence of 32.7%−34.9%, and then decreased significantly with the increasing prevalence of prediabetes. …”

The food consumption quantity. by Bingtao Su (4740009)

“…Results demonstrated that groups with a lower prevalence of diabetes showed lower dietary EFP, while the dietary EFP and GHG emissions peaked among the prediabetes group with a prevalence of 32.7%−34.9%, and then decreased significantly with the increasing prevalence of prediabetes. …”

Dataset. by Bingtao Su (4740009)

“…Results demonstrated that groups with a lower prevalence of diabetes showed lower dietary EFP, while the dietary EFP and GHG emissions peaked among the prediabetes group with a prevalence of 32.7%−34.9%, and then decreased significantly with the increasing prevalence of prediabetes. …”

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

The raw data used in this study. by Yu Fu (16582)

“…<div><p>Objectives</p><p>Hematological malignancies (HMs) pose a severe threat to human health and contribute substantially to the disease burden in mainland China and Taiwan. …”

Disease burden trends of leukemia and lymphoma analyzed by joinpoint regression analysis in mainland China. by Yu Fu (16582)

Disease burden trends of multiple myeloma and other hematological neoplasms analyzed by joinpoint regression analysis in mainland China. by Yu Fu (16582)

Projections of incidence for hematological malignancies in mainland China. by Yu Fu (16582)

Age- and sex-specific distribution of prevalence, incidence, mortality, and DALY rates for leukemia in mainland China. by Yu Fu (16582)

Projections of mortality for hematological malignancies in mainland China. by Yu Fu (16582)

Age- and sex-specific distribution of prevalence, incidence, mortality, and DALY rates for lymphoma, multiple myeloma, and other hematological neoplasms in mainland China. by Yu Fu (16582)

Descriptive characteristics of the sample. by Ronna Bañada (19233063)

“…Additionally, higher levels of ethnic identity were associated with decreased depression among the entire group. The striking results on the moderating role of ethnic identity among subgroups call for further research on the ethnic identity development of APIDA college students, to help mitigate the effects of racial discrimination within a variety of systemic, complex, and dynamic sociocultural contexts.…”

Regression models of COVID-19-related racial discrimination and depression symptoms among the full sample. by Ronna Bañada (19233063)

Regression models of COVID-19-related racial discrimination and anxiety symptoms among the full sample. by Ronna Bañada (19233063)

Penalty conversions yield large practical decreases, but robustness and generalizability of result are limited by data quality and sample selection. by Ryan J. Treves (13527735)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)