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Record W4414367374 · doi:10.1093/fshmag/vuaf083

Addenda, corrigenda, et explanenda to <i>Common and Scientific Names of Fishes</i> , Eighth Edition

2025· article· en· W4414367374 on OpenAlexaffabout
Juan J. Schmitter‐Soto, Katherine E. Bemis, Thomas E. Dowling, Lloyd T. Findley, Matthew G. Girard, Dean A. Hendrickson, Katriina L. Ilves, Katherine P. Maslenikov, Gorgonio Ruiz‐Campos, Christopher Scharpf, H. J. Walker

Bibliographic record

VenueFisheries · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsCanadian Museum of Nature
Fundersnot available
KeywordsScientific literatureToponymyProper nounScientific writing

Abstract

fetched live from OpenAlex

A few months after the publication of the eighth edition of the List of Common and Scientific Names of Fishes from the United States, Canada, and Mexico (Page et al., 2023; henceforth, the List), a relevant book appeared in electronic format, edited by the Mexican federal authorities on natural resources and biodiversity: Fishes and Lampreys of Mexico: an annotated checklist, by Fricke et al. (2024). This massive work presents more than 500 possible additions or corrections to the List; however, most are based on larvae, unverified or nonexistent records, no supporting voucher specimens and/or peer-reviewed publications, subspecies raised to species status without a published formal taxonomic revision, no evidence of establishment of exotic species, or other concerns that would need to be resolved before incorporating the additions or corrections into the List. Nevertheless, the Committee on Names of Fishes (hereafter, the Committee) is grateful for more than 200 additions and corrections to the List. The novelties are presented here (Supplementary Table 1), mostly with the common names proposed by Fricke et al. (2024), as well as their original sources. In addition, Supplementary Table 1 also includes new species and other taxonomic and nomenclatural changes proposed since the publication of the List, plus several corrections and revised spellings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.388

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 head0.013
GPT teacher head0.222
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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