Triple oxygen isotope ratios offer new opportunities to improve reconstructions of past climate by quantifying evaporation, relative humidity, and diagenesis in geologic archives. However, the utility of in paleoclimate applications is hampered by a limited understanding of how precipitation values vary across time and space. To improve applications of , we present , -excess, and data from 26 precipitation sites in the western and central United States and three streams from the Willamette River Basin in western Oregon. In this data set, we find that precipitation tracks evaporation but appears insensitive to many controls that govern variation in , including Rayleigh distillation, elevation, latitude, longitude, and local precipitation amount. Seasonality has a large effect on variation in the data set and we observe higher seasonally amount-weighted average precipitation values in the winter (40 ± 15 per meg [± standard deviation]) than in the summer (18 ± 18 per meg). This seasonal precipitation variability likely arises from a combination of sub-cloud evaporation, atmospheric mixing, moisture recycling, sublimation, and/or relative humidity, but the data set is not well suited to quantitatively assess isotopic variability associated with each of these processes. The seasonal pattern, which is absent in -excess and opposite in sign from , appears in other data sets globally; it showcases the influence of seasonality on values of precipitation and highlights the need for further systematic studies to understand variation in values of precipitation.
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http://dx.doi.org/10.1029/2022pa004458 | DOI Listing |
J Chem Inf Model
January 2025
Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marquês de São Vicente Street, Gávea, Rio de Janeiro, RJ 22451-900, Brazil.
Machine learning approaches often involve evaluating a wide range of models due to various available architectures. This standard strategy can lead to a lack of depth in exploring established methods. In this study, we concentrated our efforts on a single deep learning architecture type to assess whether a focused approach could enhance performance in fault diagnosis.
View Article and Find Full Text PDFPLOS Digit Health
January 2025
Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland.
Risk calculators based on statistical and/or mechanistic models have flourished and are increasingly available for a variety of diseases. However, in the day-to-day practice, their usage may be hampered by missing input variables. Certain measurements needed to calculate disease risk may be difficult to acquire, e.
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January 2025
Schrödinger Incorporated, Cambridge, Massachusetts 02142, United States.
J Prim Care Community Health
January 2025
University of Rome Tor Vergata, Roma, Lazio, Italy.
Introduction: Home care workers (HCWs) are paid caregivers who provide support to patients with chronic conditions and functional limitations. Additionally, they provide emotional support to patients and familial support. Although several qualitative studies have been conducted on HCWs, they focused more on studying prevalently the lived experiences about the workplace violence, the end of life, stressor and resilience, during the COVID-19 pandemic or focused more in dementia and heart failure, but not on feelings and working conditions.
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February 2025
Faculty of Civil and Environmental Engineering, Technion, Haifa 320003, Israel.
Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2].
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