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Record W2473746260 · doi:10.12705/653.43

Report of the Special Committee on Registration of Algal and Plant Names (including fossils)

2016· article· en· W2473746260 on OpenAlexaff
Mary E. Barkworth, Mark Watson, Fred R. Barrie, Irina V. Belyaeva, R. C. K. Chung, Jiřina Dašková, Gerrit Davidse, Alı A. Dönmez, Alexander B. Doweld, Stefan Dreßler, Christina Flann, Kanchi N. Gandhi, D. V. Geltman, Hugh Glen, Werner Greuter, Martin J. Head, Regine Jahn, M.K. Janarthanam, Liliana Katinas, Paul M. Kirk, Niels Klazenga, Wolf-Henning Kusber, Jiřı́ Kvaček, Valéry Malécot, David G. Mann, Karol Marhold, Hidetoshi Nagamasu, Nicky Nicolson, Alan Paton, David J. Patterson, Michelle Price, Willem F. Prud’homme van Reine, Craig W. Schneider, Alexander N. Sennikov, Gideon F. Smith, Peter F. Stevens, Zhu‐Liang Yang, Xian‐Chun Zhang, Giuseppe C. Zuccarello

Bibliographic record

VenueTaxon · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsCanadian Sleep SocietyBrock University
Fundersnot available
KeywordsHerbariumBotanical gardenGeographyArchaeologyLibrary scienceWatsonEcologyBiology

Abstract

fetched live from OpenAlex

The Special Committee on Registration of Algal and Plant Names (including fossils) was established at the XVIII International Botanical Congress (IBC) in Melbourne in 2011, its mandate being to consider what would be involved in registering algal and plant names (including fossils), using a procedure analogous to that for fungal names agreed upon in Melbourne and included as Art. 42 in the International Code of Nomenclature for algae, fungi, and plants. Because experience with voluntary registration was key to persuading mycologists of the advantages of mandatory registration, we began by asking institutions with a history of nomenclatural indexing to develop mechanisms that would permit registration. The task proved more difficult than anticipated, but considerable progress has been made, as is described in this report. It also became evident that the Nomenclature Section needs a structure that will allow ongoing discussion of registration and associated issues. Simultaneously with this report we are submitting four proposals that would provide such a structure.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.114

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 head0.050
GPT teacher head0.236
Teacher spread0.186 · 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 designObservational
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

Citations17
Published2016
Admission routes1
Has abstractyes

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