NCBITaxonomy.jl: rapid biological names finding and reconciliation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
NCBITaxonomy.jl is a Julia package designed to address the complex challenges of taxonomic name reconciliation using a local copy of the NCBI taxonomic backbone (Federhen in Nucleic Acids Res 40:D136-D143, 2012, Schoch et al. in Database 2020:baaa062, 2020). The package provides advanced name matching capabilities that handle common issues in taxonomic data, including synonyms, homonyms, vernacular names, nomenclatural changes, and typographical errors. Core functionalities include case-insensitive search, customizable fuzzy string matching, and taxonomically-restricted searches. The package implements a robust exception system that explicitly handles ambiguous matches without interrupting workflow execution, enabling automated processing of large datasets. NCBITaxonomy.jl works with Julia 1.6 and up, uses Apache Arrow format for efficient local storage. It provides lineage navigation and taxonomic distance functions. The package has been successfully deployed in large-scale projects for automated name reconciliation and cleaning, demonstrating its effectiveness for high-throughput name reconciliation across heterogeneous biological datasets. The design prioritizes programmatic access over command-line usage, making it well-suited for integration into bioinformatics pipelines requiring reliable taxonomic standardization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it