Corrigendum: What Is It Like to Be a Bass? Red Herrings, Fish Pain and the Study of Animal Sentience
Bibliographic record
Abstract
Corrigendum on: Mason G. J. & Lavery J. M. (2022). What Is It Like to Be a Bass? Red Herrings, Fish Pain and the Study of Animal Sentience. Frontiers in Veterinary Science, 9. DOI: 10.3389/fvets.2022.788289Text CorrectionIn the original article, there was a miswording. This special edition focuses on the legacy of Dr. Victoria Braithwaite, but the wording of the second sentence in our abstract “Thanks to Braithwaite's discovery of trout nociceptors, and concerns that current practices could compromise welfare in countless fish, this issue's importance is beyond dispute” accidentally implied this discovery was her achievement only, when of course it involved others too. To better sum up Dr. Braithwaite’s impact, we have now replaced “discovery of trout nociceptors” with “research leadership” in the second line of the abstract. This does not change the scientific conclusions of the article in any way. The original article has been updated.
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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.090 | 0.064 |
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".