Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0
This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables with most contributi...
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Online Access: | https://dx.doi.org/10.18738/t8/gmu4xy https://dataverse.tdl.org/citation?persistentId=doi:10.18738/T8/GMU4XY |
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ftdatacite:10.18738/t8/gmu4xy 2023-05-15T15:03:02+02:00 Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 FLUD Research Group 2020 https://dx.doi.org/10.18738/t8/gmu4xy https://dataverse.tdl.org/citation?persistentId=doi:10.18738/T8/GMU4XY unknown Texas Data Repository Dataverse dataset Dataset 2020 ftdatacite https://doi.org/10.18738/t8/gmu4xy 2021-11-05T12:55:41Z This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables with most contribution according to MaxentVariableSelection (Jueterbock, et.al. 2016) (vegetation community, inundation probability, and soil classification) and implemented as hinge features with a regularization multiplier (beta parameter) of 1.0. The value of 0p150 in the dataset file names refer to the fact that it uses digitized downed trees with structure. Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). Maximum Entropy modelling of species geographic distribution. Ecological Modelling, 190, 231-259. Jueterbock, A. Smolina, I., Cover, J.A., Hoaru, C. (2016) The fate of arctic seaweed Fucus disticus under climate change: an ecological niche modelling approach. Ecology and Evolution, 6(6), 1712-1724. Dataset Arctic Climate change DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables with most contribution according to MaxentVariableSelection (Jueterbock, et.al. 2016) (vegetation community, inundation probability, and soil classification) and implemented as hinge features with a regularization multiplier (beta parameter) of 1.0. The value of 0p150 in the dataset file names refer to the fact that it uses digitized downed trees with structure. Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). Maximum Entropy modelling of species geographic distribution. Ecological Modelling, 190, 231-259. Jueterbock, A. Smolina, I., Cover, J.A., Hoaru, C. (2016) The fate of arctic seaweed Fucus disticus under climate change: an ecological niche modelling approach. Ecology and Evolution, 6(6), 1712-1724. |
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Dataset |
author |
FLUD Research Group |
spellingShingle |
FLUD Research Group Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 |
author_facet |
FLUD Research Group |
author_sort |
FLUD Research Group |
title |
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 |
title_short |
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 |
title_full |
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 |
title_fullStr |
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 |
title_full_unstemmed |
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 |
title_sort |
maximum entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 1.0 |
publisher |
Texas Data Repository Dataverse |
publishDate |
2020 |
url |
https://dx.doi.org/10.18738/t8/gmu4xy https://dataverse.tdl.org/citation?persistentId=doi:10.18738/T8/GMU4XY |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_doi |
https://doi.org/10.18738/t8/gmu4xy |
_version_ |
1766334941655728128 |