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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0150.007

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.

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 source (direct Gemma or distilled Codex), not a consensus.

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

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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