Addenda, corrigenda, et explanenda to <i>Common and Scientific Names of Fishes</i> , Eighth Edition
Bibliographic record
Abstract
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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.209 | 0.146 |
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".