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Record W2558869320 · doi:10.12705/645.11

The foundation of the <i>Melbourne Code</i> Appendices: Announcing a new paradigm for tracking nomenclatural decisions

2015· article· en· W2558869320 on OpenAlexaff
John H. Wiersema, John McNeill, Nicholas J. Turland, Sylvia Orli, Warren L. Wagner

Bibliographic record

VenueTaxon · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsRoyal Ontario Museum
FundersAgricultural Research ServiceU.S. Department of AgricultureSmithsonian Institution
KeywordsCode (set theory)Computer scienceRelation (database)Resource (disambiguation)World Wide WebInstitutionNomenclatureLibrary scienceDatabasePolitical scienceLawEcologyTaxonomy (biology)Programming languageBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.279
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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