ArticleApplications in plant sciences2020
Recognition of Latin scientific names using artificial neural networks.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Hespi: a pipeline for automatically detecting information from herbarium specimen sheets.Bioscience · 2025Article
- Recognition of Latin scientific names using artificial neural networks.Applications in plant sciences · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseThe automated recognition of Latin scientific names within vernacular text has many applications, including text mining, search indexing, and automated specimen-label processing. Most published solutions are computationally inefficient, incapable of running within a web browser, and focus on texts in English, thus omitting a substantial portion of biodiversity literature. METHODS AND
resultsAn open-source browser-executable solution, Quaesitor, is presented here. It uses pattern matching (regular expressions) in combination with an ensembled classifier composed of an inclusion dictionary search (Bloom filter), a trio of complementary neural networks that differ in their approach to encoding text, and word length to automatically identify Latin scientific names in the 16 most common languages for biodiversity articles.
conclusionsIn combination, the classifiers can recognize Latin scientific names in isolation or embedded within the languages used for >96% of biodiversity literature titles. For three different data sets, they resulted in a 0.80-0.97 recall and a 0.69-0.84 precision at a rate of 8.6 ms/word.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.