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In the sensitivity analysis study, we used a randomly selected dataset out of the database.
This is because the DWT-based detector is more sensitive to discretization noise caused by image rotations which happen to be very common in the Oxford dataset ( out of the groups contain strong rotational variations, another contain weak rotational variations).
Validation presence = number of presence points in the validation dataset (out of 4443) Fig. 3 Projections of climatically favorable niche space across elevation under current climate, and the B1 and A2 climate change scenarios.
In order to test the universality of our method, we performed 40 PHYLORPH runs, with one species dataset out of the 21 available in FUNYBASE blasted against two to five whole genomes in each run (Table S2).
The PHYLORPH GUI allows the user to select one species protein dataset out of those available in FUNYBASE, OrthoMCL-DB or PHYLOME-T60 to BLAST (tblastn) against 2 5 genome datasets among a panel of 107 fully sequenced genomes (representing 28 orders among 14 classes within the fungal tree of life; Table S1).
For each run, the approach previously described was strictly respected: one species dataset out of the 21 available in FUNYBASE was randomly selected and used to interrogate two to five (depending on the run; see Table S2) closely related genomic resources available in PHYLORPH (same class or order, as possible).
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The proposed work is designed to generate a robust dataset of out-of-field dose measurements which can be used for the development and validation of dose algorithms.
MCC is used to evaluate the performance of the predictor when the positive and negative samples in the dataset are out-of-balance.
We built the oil and gas model using a Random Forests model with 300 bootstrap replicates or classification trees (k) and using the entire sample dataset for out-of-bag (OOB) testing with replacement.
There is a wide variety of I/O requirements: Some implementations require reading and writing large datasets, others out-of-core data access, or they have database access requirements.
MCC is always introduced when the positive and negative datasets are out-of-balance from each other.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com