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An introduction to the theory of spin glasses
Altieri, A., & Baity-Jesi, M. (2024). An introduction to the theory of spin glasses. In T. Chakraborty (Ed.), Reference module in materials science and materials engineering. Encyclopedia of condensed matter physics (pp. 361-370). https://doi.org/10.1016/B978-0-323-90800-9.00249-3
Multifractality in spin glasses
Baity-Jesi, M., Calore, E., Cruz, A., Fernández, L. A., Gil-Narvión, J. M., Pemartín, I. G. A., … Yllanes, D. (2024). Multifractality in spin glasses. Proceedings of the National Academy of Sciences of the United States of America PNAS, 121(2), e2312880120 (7 pp.). https://doi.org/10.1073/pnas.2312880120
Similar temporal patterns in insect richness, abundance and biomass across major habitat types
Gebert, F., Bollmann, K., Schuwirth, N., Duelli, P., Weber, D., & Obrist, M. K. (2024). Similar temporal patterns in insect richness, abundance and biomass across major habitat types. Insect Conservation and Diversity, 17(1), 139-154. https://doi.org/10.1111/icad.12700
The effect of water temperature changes on biological water quality assessment
Khaliq, I., Chollet Ramampiandra, E., Vorburger, C., Narwani, A., & Schuwirth, N. (2024). The effect of water temperature changes on biological water quality assessment. Ecological Indicators, 159, 111652 (10 pp.). https://doi.org/10.1016/j.ecolind.2024.111652
Harnessing computational methods to characterize chemical impacts on biodiversity
Kosnik, M. B., Schuwirth, N., & Rico, A. (2024). Harnessing computational methods to characterize chemical impacts on biodiversity. Environmental Science and Technology Letters. https://doi.org/10.1021/acs.estlett.3c00865
Gamified online surveys: Assessing experience with self-determination theory
Aubert, A. H., Scheidegger, A., & Schmid, S. (2023). Gamified online surveys: Assessing experience with self-determination theory. PLoS One, 18(10), e0292096 (20 pp.). https://doi.org/10.1371/journal.pone.0292096
A comparison of numerical approaches for statistical inference with stochastic models
Bacci, M., Sukys, J., Reichert, P., Ulzega, S., & Albert, C. (2023). A comparison of numerical approaches for statistical inference with stochastic models. Stochastic Environmental Research and Risk Assessment, 37(8), 3041-3061. https://doi.org/10.1007/s00477-023-02434-z
Memory and rejuvenation effects in spin glasses are governed by more than one length scale
Baity-Jesi, M., Calore, E., Cruz, A., Fernandez, L. A., Gil-Narvion, J. M., Gonzalez-Adalid Pemartin, I., … Yllanes, D. (2023). Memory and rejuvenation effects in spin glasses are governed by more than one length scale. Nature Physics, 19(7), 978-985. https://doi.org/10.1038/s41567-023-02014-6
Access to global wheat reserves determines country-level vulnerability to conflict-induced Ukrainian wheat supply disruption
Bertassello, L., Winters, P., & Müller, M. F. (2023). Access to global wheat reserves determines country-level vulnerability to conflict-induced Ukrainian wheat supply disruption. Nature Food, 4, 673-676. https://doi.org/10.1038/s43016-023-00806-w
Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China
Cai, S., Zuo, D., Wang, H., Xu, Z., Wang, G. Q., & Yang, H. (2023). Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China. Agricultural Water Management, 278, 108142 (16 pp.). https://doi.org/10.1016/j.agwat.2023.108142
A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation
Chollet Ramampiandra, E., Scheidegger, A., Wydler, J., & Schuwirth, N. (2023). A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation. Ecological Modelling, 481, 110353 (11 pp.). https://doi.org/10.1016/j.ecolmodel.2023.110353
Methods comparison for detecting trends in herbicide monitoring time-series in streams
Chow, R., Spycher, S., Scheidegger, R., Doppler, T., Dietzel, A., Fenicia, F., & Stamm, C. (2023). Methods comparison for detecting trends in herbicide monitoring time-series in streams. Science of the Total Environment, 891, 164226 (11 pp.). https://doi.org/10.1016/j.scitotenv.2023.164226
Seasonal drivers and risks of aquatic pesticide pollution in drought and post-drought conditions in three Mediterranean watersheds
Chow, R., Curchod, L., Davies, E., Veludo, A. F., Oltramare, C., Dalvie, M. A., … Fuhrimann, S. (2023). Seasonal drivers and risks of aquatic pesticide pollution in drought and post-drought conditions in three Mediterranean watersheds. Science of the Total Environment, 858, 159784 (12 pp.). https://doi.org/10.1016/j.scitotenv.2022.159784
Exploring signature-based model calibration for streamflow prediction in ungauged basins
Dal Molin, M., Kavetski, D., Albert, C., & Fenicia, F. (2023). Exploring signature-based model calibration for streamflow prediction in ungauged basins. Water Resources Research, 59(7), e2022WR031929 (32 pp.). https://doi.org/10.1029/2022WR031929
Denoising single MR spectra by deep learning: miracle or mirage?
Dziadosz, M., Rizzo, R., Kyathanahally, S. P., & Kreis, R. (2023). Denoising single MR spectra by deep learning: miracle or mirage? Magnetic Resonance in Medicine, 90(5), 1749-1761. https://doi.org/10.1002/mrm.29762
Challenges of spatially extrapolating aquatic pesticide pollution for policy evaluation
Fabre, C., Doppler, T., Chow, R., Fenicia, F., Scheidegger, R., Dietzel, A., & Stamm, C. (2023). Challenges of spatially extrapolating aquatic pesticide pollution for policy evaluation. Science of the Total Environment, 875, 162639 (11 pp.). https://doi.org/10.1016/j.scitotenv.2023.162639
A theoretical analysis of the learning dynamics under class imbalance
Francazi, E., Baity-Jesi, M., & Lucchi, A. (2023). A theoretical analysis of the learning dynamics under class imbalance. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of machine learning research: Vol. 202. Proceedings of the 40th international conference on machine learning (pp. 10285-10322). PMLR.
HESS opinions: are soils overrated in hydrology?
Gao, H., Fenicia, F., & Savenije, H. H. G. (2023). HESS opinions: are soils overrated in hydrology? Hydrology and Earth System Sciences, 27(14), 2607-2620. https://doi.org/10.5194/hess-27-2607-2023
Using satellite imagery to investigate Blue-Green Infrastructure establishment time for urban cooling
Gobatti, L., Bach, P. M., Scheidegger, A., & Leitão, J. P. (2023). Using satellite imagery to investigate Blue-Green Infrastructure establishment time for urban cooling. Sustainable Cities and Society, 97, 104768 (11 pp.). https://doi.org/10.1016/j.scs.2023.104768
Systematic handling of environmental fate data for model development - illustrated for the case of biodegradation half-life data
Hafner, J., Fenner, K., & Scheidegger, A. (2023). Systematic handling of environmental fate data for model development - illustrated for the case of biodegradation half-life data. Environmental Science and Technology Letters, 10(10), 859-864. https://doi.org/10.1021/acs.estlett.3c00526
 

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