Back

Mathematical characterization of fractal river networks

Terui, A.

2023-01-02 ecology
10.1101/2022.11.07.515375 bioRxiv
Show abstract

Carraro and Altermatt1 performed an extensive analysis to study scaling properties of river networks. They concluded that: (1) branching "probability" is better termed as branching "ratio" for real river networks because it is not a probability; (2) branching ratio is a scale-dependent quantity as the value changes across spatial resolutions at which river networks are extracted (expressed as the threshold catchment area AT that initiates channels or pixel size l); (3) Optimal Channel Networks most accurately predicted the metapopulation stability and capacity. However, the supporting ground for these conclusions is either seriously flawed or inconclusive due to the improper use of branching probability, scale invariance, dimensions, and units.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Scientific Reports
○ 3612 papers in training set
Top 0.1%
36.1%
Similar papers in this journal
2
Ecology and Evolution
○ 267 papers in training set
Top 1.0%
6.2%
Similar papers in this journal
3
Journal of The Royal Society Interface
● 235 papers in training set
Top 1%
4.0%
Similar papers in this journal
4
PLOS Computational Biology
● 1863 papers in training set
Top 9%
3.5%
Similar papers in this journal
5
PLOS ONE
● 5266 papers in training set
Top 36%
3.2%
Similar papers in this journal
50% of probability mass above
6
Methods in Ecology and Evolution
○ 176 papers in training set
Top 0.7%
3.2%
7
Royal Society Open Science
● 214 papers in training set
Top 1%
3.2%
8
Nature Communications
○ 5641 papers in training set
Top 36%
3.2%
9
Ecology Letters
○ 135 papers in training set
Top 0.8%
2.7%
10
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 21%
2.4%
11
Ecology
○ 85 papers in training set
Top 0.8%
2.4%
12
Science of The Total Environment
○ 186 papers in training set
Top 2%
2.1%
13
PeerJ
◐ 308 papers in training set
Top 4%
2.1%
14
Ecosphere
○ 57 papers in training set
Top 0.8%
1.7%
15
Communications Biology
○ 993 papers in training set
Top 17%
1.5%
16
PNAS Nexus
◐ 159 papers in training set
Top 2%
1.3%
17
iScience
○ 1154 papers in training set
Top 23%
1.3%
18
eLife
● 5828 papers in training set
Top 59%
1.1%
19
Patterns
○ 78 papers in training set
Top 2%
1.1%
20
Journal of Theoretical Biology
○ 162 papers in training set
Top 2%
1.0%
21
Ecography
○ 54 papers in training set
Top 1%
1.0%
22
Communications Earth & Environment
○ 14 papers in training set
Top 0.4%
0.8%
23
Frontiers in Ecology and Evolution
○ 69 papers in training set
Top 3%
0.8%
24
Physics of Fluids
● 13 papers in training set
Top 0.1%
0.6%
25
Ecological Modelling
○ 28 papers in training set
Top 0.6%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.