Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

“…On average, meliponiculturists lost 43.4 % of their stingless bee colonies annually, 33.3 % during the rainy season, and 22.0 % during the dry season. We found that colony losses during the rainy season decreased with higher abundance of forested areas and increased with higher abundance of agricultural area around meliponaries. …”

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. …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

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)

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. …”

<b>Aversive learning decreases aversive-reinforcer sensitivity</b><b> </b><b>in honey bees</b> by Martin GIURFA (8649351)

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)

Data Sheet 1_Large language models for closed-library multi-document query, test generation, and evaluation.docx by Claire Randolph (19747105)

“…Knowledge tests need to be generated on new material and existing tests revised, tracking knowledge base updates. Large Language Models (LLMs) provide a framework for artificial intelligence-assisted knowledge acquisition and continued learning. …”

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Overview of the WeARTolerance program. by Ana Beato (20489933)

“…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”

The flowchart of the large model for font style transfer. by Weijia Zhu (65481)

Subjects:

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)

The PRAME-AS knockout cells showed a decrease in migration, proliferation, stemness, and viability. by Zahra Hosseininia (22265113)

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

“…<b>(b)</b> Ophthalmologists’ decision time decreases overall when the deep learning model makes the correct decision. …”

Table 1_Effect of decreased suspended sediment content on chlorophyll-a in Dongting Lake, China.docx by Le Zhang (88249)

“…The reduction in SSC may influence chlorophyll-a (Chl-a) concentrations in water, thereby further affecting the aquatic ecological environment. …”

Bidimensional High-Low Frequency (HF, LF) plane representation of the interaction among Phase and High-Low Clarity Groups_: In the Low Clarity (LC) group there was a larg... by David Vagni (8060432)

Subjects:

Feature engineering. by Youli Jiang (15249070)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”

Machine Learning Models for High Explosive Crystal Density and Performance by Jack V. Davis (9175514)

“…This improvement in model performance relative to past works is attributed to our handling of chiral-specified Simplified Molecular-Input Line-Entry System (SMILES) strings and introduction of a new molecular descriptor, MolDensity. To elucidate descriptor importance, we evaluated interpretable descriptors in terms of importance and compared the accuracy of a statistics-driven machine learning model against a model comprised of descriptors typically assumed to control material density. …”