Dual nomenclature to be supported explicitly in the International Code of Nomenclature for algae, fungi, and plants
Bibliographic record
Abstract
Dual nomenclature as applied to dinoflagellates is underpinned by conceptual and practical considerations. It allows the separate naming of fossil- and non-fossil species even when they are linked to one another by incubation studies and other techniques. It is needed because fossil- and non-fossil taxonomies are based on different stages of the life cycle and cannot be integrated at the generic level. All taxonomists today who study dinoflagellates, whether living or fossil, place their work under the International Code of Nomenclature for algae, fungi, and plants. The Shenzhen Code and its predecessors have supported dual nomenclature implicitly with the help of examples, but without clear explanation of what it is and how it works. In Madrid, Spain in July 2024, the Nomenclature Section of the XX International Botanical Congress approved two new articles for the Code that remove earlier contradictions and introduced dual nomenclature explicitly, drawing on a critical distinction between ‘synonymy’ and the new concept and term ‘taxonomic equivalence’. These changes will be incorporated into the forthcoming Madrid Code. In addition to placing the Code in its historical context, we explain how it is amended in order to demystify an intricate but important procedure.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".