Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Jarmo Mäkelä"'
Autor:
Jarmo Mäkelä
Publikováno v:
Entropy, Vol 13, Iss 7, Pp 1324-1354 (2011)
We consider a microscopic model of a stretched horizon of the Schwarzschild black hole. In our model the stretched horizon consists of a finite number of discrete constituents. Assuming that the quantum states of the Schwarzschild black hole are enco
Externí odkaz:
https://doaj.org/article/c4f1f870848348279c176f4c8dc6096c
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 16:162-186
Model selection is one of the most central tasks in supervised learning. Validation set methods are the standard way to accomplish this task: models are trained on training data, and the model with the smallest loss on the validation data is selected
Autor:
Jarmo Mäkelä, Laura Arppe, Hannu Fritze, Jussi Heinonsalo, Kristiina Karhu, Jari Liski, Markku Oinonen, Petra Straková, Toni Viskari
Publikováno v:
Biogeosciences. 19:4305-4313
Soils account for the largest share of carbon found in terrestrial ecosystems, and their status is of considerable interest for the global carbon cycle budget and atmospheric carbon concentration. The decomposition of soil organic matter depends on e
The stomata on the leaves of terrestrial plants are a crucial pathway both in the soil-plant-atmosphere hydrological continuum and in the global carbon cycle. Stomatal optimization approaches have proven to be relevant in modelling the trade-off betw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f3225c28ff459fc1def62d064f082a4
https://doi.org/10.5194/egusphere-egu23-13247
https://doi.org/10.5194/egusphere-egu23-13247
Autor:
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, Kai Puolamäki
Publikováno v:
Biogeosciences. 19:2095-2099
In this note, we argue that the outputs of causal discovery algorithms should not usually be considered end results but rather starting points and hypotheses for further study. The incentive to explore this topic came from a recent study by Krich et
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031264214
We introduce a Python library, called slisemap, that contains a supervised dimensionality reduction method that can be used for global explanation of black box regression or classification models. slisemap takes a data matrix and predictions from a b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::919d68148d0f426ff910932c9b01c319
http://hdl.handle.net/10138/356701
http://hdl.handle.net/10138/356701
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global explanations for all data items, but these explanations generally have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55b01b9c3f39c1a9132bb87d5e0274e3
http://hdl.handle.net/10138/353435
http://hdl.handle.net/10138/353435
Implementation and initial calibration of carbon-13 soil organic matter decomposition in Yasso model
Autor:
Jussi Heinonsalo, Markku Oinonen, Toni Viskari, Hannu Fritze, Jarmo Mäkelä, Petra Straková, Jari Liski, Laura Arppe
Soil carbon sequestration has gained traction as a mean to mitigate rising atmospheric carbon dioxide concentrations. Verification of different methods’ efficiency to increase soil carbon sink requires, in addition to good quality measurements, rel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c45088d77e77e22af6e3dc941cdc739
https://bg.copernicus.org/preprints/bg-2021-327/
https://bg.copernicus.org/preprints/bg-2021-327/