Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0

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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Bibliographic Details
Main Author: FLUD Research Group
Format: Dataset
Language:unknown
Published: Texas Data Repository Dataverse 2020
Subjects:
Arctic
Climate change
Online Access:https://dx.doi.org/10.18738/t8/ilrkfq
https://dataverse.tdl.org/citation?persistentId=doi:10.18738/T8/ILRKFQ
id ftdatacite:10.18738/t8/ilrkfq
record_format openpolar
spelling ftdatacite:10.18738/t8/ilrkfq 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 7.0 FLUD Research Group 2020 https://dx.doi.org/10.18738/t8/ilrkfq https://dataverse.tdl.org/citation?persistentId=doi:10.18738/T8/ILRKFQ unknown Texas Data Repository Dataverse dataset Dataset 2020 ftdatacite https://doi.org/10.18738/t8/ilrkfq 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 7.0. 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
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
description 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 7.0. 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.
format 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 7.0
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 7.0
title_short Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0
title_full Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0
title_fullStr Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0
title_full_unstemmed Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0
title_sort maximum entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 7.0
publisher Texas Data Repository Dataverse
publishDate 2020
url https://dx.doi.org/10.18738/t8/ilrkfq
https://dataverse.tdl.org/citation?persistentId=doi:10.18738/T8/ILRKFQ
geographic Arctic
geographic_facet Arctic
genre Arctic
Climate change
genre_facet Arctic
Climate change
op_doi https://doi.org/10.18738/t8/ilrkfq
_version_ 1766334941816160256

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