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    In:  Blumea: Biodiversity, Evolution and Biogeography of Plants vol. 63, pp. 31-53
    Publication Date: 2024-01-12
    Description: In preparing the treatment of Dracaena for Flore du Gabon and Flore d\xe2\x80\x99Afrique centrale, a relatively high number of taxonomic and nomenclatural novelties were discovered; these are presented here. Within Dracaena five species and one forma are described as new, D. bushii, D. haemanthoides, D. marina, D. wakaensis, D. waltersiae and D. laxissima forma aureilicia. Each new species is provided with a full description and taxonomic notes. Apart from that, five species are reinstated, D. braunii, D. nitens, D. perrottetii, D. tholloniana and D. usambarensis. A further 23 names are treated here as a synonym for the first time: D. bequaertii, D. buettneri, D. cuspidibracteata, D. densifolia, D. gabonica, D. gazensis, D. ledermannii, D. letestui, D. litoralis, D. longipetiolata, D. monostachya var. angolensis, D. oddonii, D. perrottetii var. minor, D. poggei, D. pseudoreflexa, D. reflexa var. buchneri, D. rubroaurantiaca, D. soyauxiana, D. talbotii, D. tessmannii, D. usambarensis var. longifolia, D. vanderystii and Pleomele heudelotii, while for four names a neotype and for 14 names a lectotype has been designated. Distribution maps are provided for a total of 23 species. An index of taxon names is included.
    Keywords: Africa ; Central Africa ; Dracaena ; Gabon ; Lucky Bamboo ; new species ; taxonomy
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 2
    Publication Date: 2023-01-17
    Description: High-quality data is necessary for modern machine learning. However, the acquisition of such data is difficult due to noisy and ambiguous annotations of humans. The aggregation of such annotations to determine the label of an image leads to a lower data quality. We propose a data-centric image classification benchmark with ten real-world datasets and multiple annotations per image to allow researchers to investigate and quantify the impact of such data quality issues. With the benchmark we can study the impact of annotation costs and (semi-)supervised methods on the data quality for image classification by applying a novel methodology to a range of different algorithms and diverse datasets. Our benchmark uses a two-phase approach via a data label improvement method in the first phase and a fixed evaluation model in the second phase. Thereby, we give a measure for the relation between the input labeling effort and the performance of (semi-)supervised algorithms to enable a deeper insight into how labels should be created for effective model training. Across thousands of experiments, we show that one annotation is not enough and that the inclusion of multiple annotations allows for a better approximation of the real underlying class distribution. We identify that hard labels can not capture the ambiguity of the data and this might lead to the common issue of overconfident models. Based on the presented datasets, benchmarked methods, and analysis, we create multiple research opportunities for the future directed at the improvement of label noise estimation approaches, data annotation schemes, realistic (semi-)supervised learning, or more reliable image collection.
    Type: Article , NonPeerReviewed
    Format: text
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