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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".