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De Baets, Bernard
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Vafeiadou, A-M.; Geldhof, K.; Barhdadi, W.; Rigaux, A.; Baetens, J.M.; De Baets, B.; Moens, T.; Daly, A.J.; Marine Biology Research Group, Department of Biology; Department of Data Analysis and Mathematical Modelling. Ghent University: Belgium; (2024): Experimental dataset of temperature-driven competition dynamics experiments. Marine Data Archive., meer

Publicaties (32)	Top \| Publicaties \| Dataset
A1 publicaties (25) [show]
Vafeiadou, A.-M.; Geldhof, K.; Barhdadi, W.; Baetens, J.M.; De Baets, B.; Moens, T.; Daly, A.J. (2024). Temperature-driven dynamics: unraveling the impact of climate change on cryptic species interactions within the Litoditis marina complex. PeerJ 12: e17324. https://dx.doi.org/10.7717/peerj.17324, meer Delva, S.; De Baets, B.; Baetens, J.M.; De Clerck, O.; Stock, W. (2023). No bacterial-mediated alleviation of thermal stress in a brown seaweed suggests the absence of ecological bacterial rescue effects. Sci. Total Environ. 876: 162532. https://dx.doi.org/10.1016/j.scitotenv.2023.162532, meer Avila-Alonso, D.; Baetens, J.M.; Cardenas, R.; De Baets, B. (2021). Oceanic response to the consecutive Hurricanes Dorian and Humberto (2019) in the Sargasso Sea. Nat. Hazards Earth Syst. Sci. 21(2): 837-859. https://hdl.handle.net/10.5194/nhess-21-837-2021, meer Daly, A.J.; De Meester, N.; Baetens, J.M.; Moens, T.; De Baets, B. (2021). Untangling the mechanisms of cryptic species coexistence in a nematode community through individual-based modelling. Oikos (Kbh.) 130(4): 587-600. https://hdl.handle.net/10.1111/oik.07989, meer Kuuliala, L.; Pérez-Fernández, R.; Tang, M.; Vanderroost, M.; De Baets, B.; Devlieghere, F. (2021). Probabilistic topic modelling in food spoilage analysis: a case study with Atlantic salmon (Salmo solar). Intern. J. Food Microbiol. 337: 108955. https://hdl.handle.net/10.1016/j.ijfoodmicro.2020.108955, meer Stock, M.; Nguyen, B.; Courtens, W.; Verstraete, H.; Stienen, E.; De Baets, B. (2021). Otolith identification using a deep hierarchical classification model. Comput. Electron. Agric. 180: 105883. https://dx.doi.org/10.1016/j.compag.2020.105883, meer Wang, G.; Van Stappen, G.; De Baets, B. (2021). Automated detection and counting of Artemia using U-shaped fully convolutional networks and deep convolutional networks. Exp. Syst. Appl. 171: 114562. https://hdl.handle.net/10.1016/j.eswa.2021.114562, meer Avila-Alonso, D.; Baetens, J.M.; Cardenas, R.; De Baets, B. (2020). Spatio-temporal variability of oceanographic conditions in the Exclusive Economic Zone of Cuba. J. Mar. Syst. 212: 103416. https://dx.doi.org/10.1016/j.jmarsys.2020.103416, meer Avila-Alonso, D.; Baetens, J.M.; Cardenas, R.; De Baets, B. (2020). Oceanic response to Hurricane Irma (2017) in the Exclusive Economic Zone of Cuba and the eastern Gulf of Mexico. Ocean Dynamics 70(5): 603-619. https://dx.doi.org/10.1007/s10236-020-01350-y, meer Sader, M.; Pérez-Fernández, R.; Kuuliala, L.; Devlieghere, F.; De Baets, B. (2020). The constrained median: a way to incorporate side information in the assessment of food samples. Mathematics 8(3): 406. https://hdl.handle.net/10.3390/math8030406, meer Wang, G.; Van Stappen, G.; De Baets, B. (2020). Automated Artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels. Comput. Electron. Agric. 168: 105102. https://hdl.handle.net/10.1016/j.compag.2019.105102, meer Avila-Alonso, D.; Baetens, J.M.; Cardenas, R.; De Baets, B. (2019). The impact of hurricanes on the oceanographic conditions in the Exclusive Economic Zone of Cuba. Remote Sens. Environ. 233: 111339. https://dx.doi.org/10.1016/j.rse.2019.111339, meer Kuuliala, L.; Sader, M.; Solimeo, A.; Pérez-Fernández, R.; Vanderroost, M.; De Baets, B.; De Meulenaer, B.; Ragaert, P.; Devlieghere, F. (2019). Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression. Intern. J. Food Microbiol. 303: 46-57. https://dx.doi.org/10.1016/j.ijfoodmicro.2019.04.011, meer Nguyen, B.; Rubbens, P.; Kerckhof, F.-M.; Boon, N.; De Baets, B.; Waegeman, W. (2019). Learning single‐cell distances from cytometry data. Cytometry A 95(7): 782-791. https://dx.doi.org/10.1002/cyto.a.23792, meer Kuuliala, L.; Al Hage, Y.; Ioannidis, A.-G.; Sader, M.; Kerckhof, F.-M.; Vanderroost, M.; Boon, N.; De Baets, B.; De Meulenaer, B.; Ragaert, P.; Devlieghere, F. (2018). Microbiological, chemical and sensory spoilage analysis of raw Atlantic cod (Gadus morhua) stored under modified atmospheres. Food Microbiol. 70: 232-244. https://dx.doi.org/10.1016/j.fm.2017.10.011, meer Avila-Alonso, D.; Baetens, J.M.; Cardenas, R.; De Baets, B. (2017). Assessing the effects of ultraviolet radiation on the photosynthetic potential in Archean marine environments. International Journal of Astrobiology 16(3): 271-279. https://dx.doi.org/10.1017/S147355041600032X, meer Avila-Alonso, D.; Baetens, J.M.; Cardenas, R.; De Baets, B. (2017). Modeling the effect of ultraviolet radiation on the photosynthetic potential of Prochlorococcus and Synechococcus cyanobacteria. Aquat. Microb. Ecol. 79(2): 149-164. https://dx.doi.org/10.3354/ame01822, meer Fukuda, S.; De Baets, B.; Mouton, A.M.; Waegeman, W.; Nakajima, J.; Mukai, T.; Hiramatsu, K.; Onikura, N. (2011). Effect of model formulation on the optimization of a genetic Takagi–Sugeno fuzzy system for fish habitat suitability evaluation. Ecol. Model. 222(8): 1401-1413. http://dx.doi.org/10.1016/j.ecolmodel.2011.01.023, meer Mouton, A.M.; Alcaraz-Hernández, J.D.; De Baets, B.; Goethals, P.L.M.; Martínez-Capel, F. (2011). Data-driven fuzzy habitat suitability models for brown trout in Spanish Mediterranean rivers. Environ. Model. Softw. 26(5): 615-622. dx.doi.org/10.1016/j.envsoft.2010.12.001, meer Mouton, A.M.; De Baets, B.; Goethals, P.L.M. (2010). Ecological relevance of performance criteria for species distribution models. Ecol. Model. 221(16): 1995-2002. http://dx.doi.org/10.1016/j.ecolmodel.2010.04.017, meer Mouton, A.M.; De Baets, B.; Van Broekhoven, E.; Goethals, P.L.M. (2009). Prevalence-adjusted optimisation of fuzzy models for species distribution. Ecol. Model. 220(15): 1776-1786. http://dx.doi.org/10.1016/j.ecolmodel.2009.04.020, meer Mouton, A.M.; Jowett, I.; Goethals, P.L.M.; De Baets, B. (2009). Prevalence-adjusted optimisation of fuzzy habitat suitability models for aquatic invertebrate and fish species in New Zealand. Ecological Informatics 4(4): 215-225. https://dx.doi.org/10.1016/j.ecoinf.2009.07.006, meer Mouton, A.M.; De Baets, B.; Goethals, P.L.M. (2009). Knowledge-based versus data-driven fuzzy habitat suitability models for river management. Environ. Model. Softw. 24(8): 982-993. https://dx.doi.org/10.1016/j.envsoft.2009.02.005, meer Derelle, E.; Ferraz, C.; Rombauts, S.; Rouzé, P.; Worden, A.Z.; Robbens, S.; Partensky, F.; Degroeve, S.; Echeynié, S.; Cooke, R.; Saeys, Y.; Wuyts, J.; Jabbari, K.; Bowler, C.; Panaud, O.; Piégu, B.; Ball, S.G.; Ral, J.-P.; Bouget, F.-Y.; Piganeau, G.; De Baets, B.; Picard, A.; Delseny, M.; Demaille, J.; Van de Peer, Y.; Moreau, H. (2006). Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features. Proc. Natl. Acad. Sci. U.S.A. 103(31): 11647-11652. https://dx.doi.org/10.1073/pnas.0604795103, meer Van Broekhoven, E.; Adriaenssens, V.; De Baets, B.; Verdonschot, P.F.M. (2006). Fuzzy rule-based macroinvertebrate habitat suitability models for running waters. Ecol. Model. 198(1-2): 71-84. https://dx.doi.org/10.1016/j.ecolmodel.2006.04.006, meer
Peer reviewed publicaties (2) [show]
Fukuda, S.; Waegeman, W.; Mouton, A.; De Baets, B. (2011). Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons, in: Melo-Pinto, P. et al. Eurofuse 2011: Workshop on Fuzzy Methods for Knowledge-Based Systems. Advances in Intelligent and Soft Computing, 107: pp. 375-387. http://dx.doi.org/10.1007/978-3-642-24001-0_34, meer Melo-Pinto, P.; Couto, P.; Serôdio, C.; Fodor, J.; De Baets, B. (Ed.) (2011). Eurofuse 2011: Workshop on Fuzzy Methods for Knowledge-Based Systems. Advances in Intelligent and Soft Computing, 107. Springer: Berlin. ISBN 978-3-642-24001-0. XII, 426 pp. http://dx.doi.org/10.1007/978-3-642-24001-0, meer
Abstracts (2) [show]
Delva, S.; Bafort, Q.; Bogaert, K.; Baetens, J.M.; De Baets, B.; De Clerck, O. (2022). Beyond genetics, exploring the contribution of epigenetics to thermal adaptation in brown seaweeds, in: Mees, J. et al. Book of abstracts – VLIZ Marine Science Day, Online event 2 March 2022. VLIZ Special Publication, 88: pp. 4, meer Chawla, S.; Desmet, N.; Raymaekers, D.; Knaeps, E.; Seuntjens, P.; De Baets, B.; Verhoest, N. (2013). Systematic sampling strategy for monitoring water quality by integrating in-situ and remote sensing data, in: Mees, J. et al. (Ed.) Book of abstracts – VLIZ Young Marine Scientists' Day. Brugge, Belgium, 15 February 2013. VLIZ Special Publication, 63: pp. 23, meer
Overige publicaties (3) [show]
Lievens, H.; Lambot, S.; Minet, J.; Vernieuwe, H.; Matgen, P.; Giustarini, L.; Plaza, D.; Verhoest, N.E.C.; Vanclooster, M.; De Baets, B.; Hoffmann, L.; Pauwels, V.R.N. (2011). Integrating radar remote sensing, hydrologic and hydraulic modelling for surface water management [POSTER]. Universiteit Gent: Gent. 1 poster pp., meer Peters, J.; Verhoest, N.E.C.; De Baets, B.; De becker, P.; Huybrechts, W.; Samson, R. (2007). Regressie- en classificatietechnieken in hydro-ecologische modellen: toepassing voor vallei-ecosystemen in Vlaanderen, in: Congres Watersysteemkennis 2006/2007: De interactie tussen bodem, grondwater en ecosysteem. Water: Tijdschrift over Integraal Waterbeleid, 31: pp. 40-44, meer Peters, J.; Verhoest, N.E.C.; De Baets, B.; Samson, R.; Huybrechts, W.; De becker, P. (2007). Hydro-ecologische modellering door middel van de 'random forest' techniek. Water: Tijdschrift over Integraal Waterbeleid 31, meer

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