The foundation of the <i>Melbourne Code</i> Appendices: Announcing a new paradigm for tracking nomenclatural decisions
Bibliographic record
Abstract
Abstract A newly expanded digital resource exists for tracking decisions on all nomenclature proposals potentially contributing to Appendices II–VIII of the International Code of Nomenclature for algae, fungi, and plants. This system owes its origins to the Smithsonian Institution's “Proposals and Disposals” website created by Dan H. Nicolson to track conservation/rejection proposals, but now also treats proposals to suppress works or requests for binding decisions. The new resource was created to accommodate the steadily expanding content of the Appendices in relation to the main body of the Code . A database is now available to generate these Appendices, as has been done for the Melbourne Code . A web interface allows users to query database content in various ways to review proposal histories or to extract all or part of the Appendices. An analysis of the underlying data was conducted to make comparisons between proposals submitted for the various editions of the Code . These include the type of nomenclatural remedy sought, the major group concerned, the numbers of names involved, the timeliness of the proposal evaluation process, the proposal success rate, and the diversity of proposal authorship. The success of proposals was also evaluated by the type of remedy sought and by major groups.
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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 | 0.036 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".