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Introducing Large Language Models to Human-Based Etymological Classification in Zooplankton

haruto, s.; keito, i.; kota, n.; takumi, t.

2025-05-09 microbiology
10.1101/2025.05.08.652882 bioRxiv
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Short AbstractWe benchmarked LLM-based against expert epithet-etymology labelling for 989 zooplankton species, finding high concordance except in culturally nuanced names, confirming LLMs value as rapid screening aids.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
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Top 0.1%
12.3%
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2
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8
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Top 0.2%
2.5%
50% of probability mass above
9
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Top 40%
2.5%
10
Methods in Ecology and Evolution
○ 176 papers in training set
Top 0.8%
2.5%
11
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12
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Top 0.7%
1.8%
13
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Top 0.6%
1.8%
14
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Top 7%
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15
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Top 4%
1.4%
16
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Top 3%
1.4%
17
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○ 73 papers in training set
Top 0.7%
1.2%
18
Aquaculture
○ 31 papers in training set
Top 0.3%
1.2%
19
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.8%
1.2%
20
Frontiers in Ecology and Evolution
○ 69 papers in training set
Top 2%
1.1%
21
Science of The Total Environment
○ 186 papers in training set
Top 3%
1.0%
22
Ecological Indicators
○ 21 papers in training set
Top 0.5%
1.0%
23
Molecular Ecology
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Top 4%
0.9%
24
Environmental DNA
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Top 0.6%
0.9%
25
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26
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Top 0.5%
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27
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Top 0.8%
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28
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● 242 papers in training set
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29
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30
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"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.