Tightening the requirements for species diagnoses would help integrate DNA-based descriptions in taxonomic practice
Bibliographic record
Abstract
Modern advances in DNA sequencing hold the promise of facilitating descriptions of new organisms at ever finer precision but have come with challenges as the major Codes of bionomenclature contain poorly defined requirements for species and subspecies diagnoses (henceforth, species diagnoses), which is particularly problematic for DNA-based taxonomy. We, the commissioners of the International Commission on Zoological Nomenclature, advocate a tightening of the definition of "species diagnosis" in future editions of Codes of bionomenclature, for example, through the introduction of requirements for specific information on the character states of differentiating traits in comparison with similar species. Such new provisions would enhance taxonomic standards and ensure that all diagnoses, including DNA-based ones, contain adequate taxonomic context. Our recommendations are intended to spur discussion among biologists, as broad community consensus is critical ahead of the implementation of new editions of the International Code of Zoological Nomenclature and other Codes of bionomenclature.
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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 | 0.131 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".