Challenges with using names to link digital biodiversity information
Bibliographic record
Abstract
The need for a names-based cyber-infrastructure for digital biology is based on the argument that scientific names serve as a standardized metadata system that has been used consistently and near universally for 250 years.As we move towards data-centric biology, name-strings can be called on to discover, index, manage, and analyze accessible digital biodiversity information from multiple sources.Known impediments to the use of scientific names as metadata include synonyms, homonyms, mis-spellings, and the use of other strings as identifiers.We here compare the name-strings in GenBank, Catalogue of Life (CoL), and the Dryad Digital Repository (DRYAD) to assess the effectiveness of the current names-management toolkit developed by Global Names to achieve interoperability among distributed data sources.New tools that have been used here include Parser (to break name-strings into component parts and to promote the use of canonical versions of the names), a modified TaxaMatch fuzzy-matcher (to help manage typographical, transliteration, and OCR errors), and Cross-Mapper (to make comparisons among data sets).The data sources include scientific names at multiple ranks; vernacular (common) names; acronyms; strain identifiers and other surrogates including idiosyncratic abbreviations and concatenations.About 40% of the name-strings in GenBank are scientific names representing about 400,000 species or infraspecies and their synonyms.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.121 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.022 | 0.053 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".