Analysis of tea output value distribution and growth- decrease rate of provinces. by Kun Qin (177235)

Juvenile demyelination leads to a substantial decrease in potassium currents in PV interneurons of the PFC. by Sara Hijazi (21656615)

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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)

The soil bacterial community richness and diversity in different tea varieties root zone soil. by Jun Sun (48981)

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LEfSe analysis of root zone soil bacterium from different tea varieties. by Jun Sun (48981)

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Relative abundance of bacteria phyla (a) and classes (b) in root zone soil of different tea varieties. by Jun Sun (48981)

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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. …”

The physical and chemical properties of root zone soil. by Jun Sun (48981)

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RDA and pearson correlation analysis of soil bacterial communities and soil environmental factors. by Jun Sun (48981)

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Prediction of bacterial BugBase phenotype. by Jun Sun (48981)

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KEGG enrichment map of differential metabolites (a) and differential abundance score map (b). by Jun Sun (48981)

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PCA of bacterial communities. by Jun Sun (48981)

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Alpha diversity, differential Metabolites, KEG DA score, lefse, Metabolites (all), OTUs, PCA components, RDA result.VIF and result. RDA. envfit. by Jun Sun (48981)

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Detected metabolites (a), volcano plot (b) and scatter plot (c) of differential metabolites. by Jun Sun (48981)

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Correlation of differential metabolites. by Jun Sun (48981)

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OUT Venn diagram. by Jun Sun (48981)

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

“…After ChatGPT-4.5 was publicly released, reliability decreased notably (e.g. ICC = −0.046 for BVMT-R Trial 3), and average scoring discrepancies per test increased (e.g. …”

Side angle tea picking. by Chunhua Yang (346871)

“…<div><p>This study proposes the S-YOLOv10-ASI algorithm to improve the accuracy of tea identification and harvesting by robots, integrating a slice-assisted super-reasoning technique. …”

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

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Mitigation (decreasing) scenario. by Fabio Correddu (12501799)