Feature importance of the extracted characteristics from the simulation using Mean Decrease in Impurity (MDI). by Simon Bin Akter (17951860)

Mean squared error of estimates decreases with larger datasets. by Alexander Eugene Zarebski (12078347)

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. …”

MS decreases the larger amplitude distribution in adult rats. by Jesús David Ayala-Rodríguez (17364296)

Reuse of paths within years (i.e., their thickness, a proxy for the directedness of movement) decreased in response to wildfire despite an increase in overall number of paths. by Aishwarya Subramanian (14930268)

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

“…</p><p><br></p><p dir="ltr">#METADATA</p><p dir="ltr">#'data.frame': 472 obs. of 28 variables:</p><p dir="ltr"> #$ ID: Factor variable; a unique identity for the response to the survey</p><p dir="ltr"> #$ Year: Factor variable; six factors available (2016, 2017, 2018, 2019, 2020, 2021) representing the year for the response to the survey</p><p dir="ltr"> #$ N_dead_annual: Numeric variable; representing the number of colonies annually lost</p><p dir="ltr">#$ N_alive_annual: Numeric variable; representing the number of colonies annually alive</p><p dir="ltr"> #$ N_dead_dry: Numeric variable; representing the number of colonies lost during the dry season</p><p dir="ltr">#$ N_alive_dry: Numeric variable; representing the number of colonies alive during the dry season</p><p dir="ltr"> #$ N_dead_rainy: Numeric variable; representing the number of colonies lost during the rainy season</p><p dir="ltr">#$ N_alive_rainy: Numeric variable; representing the number of colonies alive during the rainy season</p><p dir="ltr"> #$ Education: Factor variable; four factors are available ("Self-taught","Learned from another melip","Intro training","Formal tech training"), representing the training level in meliponiculture</p><p dir="ltr"> #$ Operation_Size: Numeric variable; representing the number of colonies managed by the participant (in n)</p><p dir="ltr"> #$ propAgri: Numeric variable; representing the percentage of agricultural area surrounding the meliponary (in %)</p><p dir="ltr"> #$ propForest: Numeric variable; representing the percentage of forested area surrounding the meliponary (in %)</p><p dir="ltr">#$ temp.avg_annual: Numeric variable; representing the average annual temperature (in ºC)</p><p dir="ltr">#$ precip_annual_sum: Numeric variable; representing the total accumulated precipitation (in mm)</p><p dir="ltr">#$ precip_Oct_March_sum: Numeric variable; representing the total accumulated precipitation between October to March (in mm)</p><p dir="ltr">#$ precip_Apri_Sept_sum: Numeric variable; representing the total accumulated precipitation between April to September (in mm)</p><p dir="ltr">#$ temp.avg_Oct_March: Numeric variable; representing the total accumulated precipitation between October to March (in ºC)</p><p dir="ltr">#$ temp.avg_Apri_Sept: Numeric variable; representing the total accumulated precipitation between April to September (in ºC)</p><p dir="ltr"> #$ Importance_dead: Factor variable; three factors are available Normal","High","Very high"), representing the perception of the significance of annual colony losses</p><p dir="ltr"> #$ Climatic_environmental: Binary variable; representing if the participant considered climatic and environmental problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Contamination: Binary variable; representing if the participant considered contamination problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Nutritional: Binary variable; representing if the participant considered nutritional problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Sanitary: Binary variable; representing if the participant considered sanitary problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Queen: Binary variable; representing if the participant considered queen problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Time: Binary variable; representing if the participant considered time problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Economic: Binary variable; representing if the participant considered economic problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Attacks: Binary variable; representing if the participant considered time attacks as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Swarming: Binary variable; representing if the participant considered swarming problems as a potential driver (1) or not (0) of their annual colony losses</p><p><br></p>…”

A noxious temperature decrease without a prior increase produces a subtly larger decrease in pain intensity. by Benedict J. Alter (9745466)

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 travel-related restrictions and mobility data for 34 countries in Europe, showing periods of increasing (red) and decreasing (green) restrictions. by Barry Smyth (10826056)

Subjects:

Phase 2 pre- and post-test Means (<i>M</i>), Standard Deviations (<i>SD</i>), between group effect sizes (<i>d</i>), and RMANOVA results (<i>F</i>-value, <i>p</i>-value) for interv... by Wendy C. Crone (2262169)

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The 452 registrations of the 750 feature vectors after dimension reduction using the AI algorithm autoencoder, the 6 clusters obtained with the DBSCAN clustering algorithm are colo... by Gerhard Fritsch (405224)

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Data_Sheet_1_Perceptual Priming Can Increase or Decrease With Aging.PDF by Kalathupiriyan A. Zhivago (9659504)

“…Here, we compared implicit perceptual memory and explicit memory measures in three groups of participants: (1) 59 healthy young volunteers (20–30 years); (2) 269 healthy old volunteers (50–90 years) and (3) 21 patients with mild cognitive impairment, i.e., MCI (50–90 years). …”

The estimated effect of 1-kg/m² increase in BMI on QALYs per year, across BMI levels. by Sean Harrison (1906282)

Subjects:

The effect of 1-kg/m² increase in BMI on total healthcare costs per year, across BMI levels. by Sean Harrison (1906282)

Subjects:

The carotid canal area is larger on the unaffected side than on the affected side in patients with unilateral moyamoya disease, but the difference decreases according to the contralateral progression. by Yuki Oichi (11840776)

“…Contralateral progression was first detected at the age of 15 years. At 16 years of age, magnetic resonance angiography showed narrowing of the anterior cerebral artery. …”

Average number of diagnoses per 1,000 patient years during the first year of the Corona pandemic and percentage change in diagnose incidence relative to the previous two years alon... by Steve Van den Bulck (5798771)

Subjects:

Increasing 2-year increments in duration of statin therapy demonstrate increasing median CACS. by Kyari Sumayin Ngamdu (16699023)

Yearly GDP, HIPC and HEPC per capita (All in current MYR). by Ridzuan Kunji Koya (20234865)

Subjects:

Adjusted hazard ratio* of mortality according to transplant calendar year (per one-year increase). by Andreas Søborg (13149494)

Subjects: