Indeterminate body growth and lack of gonadal decline in the brown ghost knifefish (<i>Apteronotus</i><i>leptorhynchus</i>), an organism exhibiting negligible brain senescence
Bibliographic record
Abstract
The brown ghost knifefish (Apteronotus leptorhynchus (Ellis in Eigenmann, 1912)) is the only vertebrate organism identified thus far that exhibits negligible brain senescence. The present study examines the basic growth patterns of this species, testing the hypothesis that indeterminate growth and lack of reproductive senescence correlate with negligible senescence. Analysis of length–mass relationships revealed negative allometric growth in males and isometric growth in females. Total length at first sexual maturity was 13.5 cm in males and 12.0 cm in females, whereas gonadal mass was 0.02 g in males and 0.2 g in females. Modelling of total length as a function of the number of otolith rings using attenuating growth equations revealed that lengths of up to 26.8 cm in males and 20.2 cm in females can be reached, indicating that the fish continue to grow throughout life. Gonadal mass increased significantly with age in sexually immature individuals of both sexes. In sexually mature fish, gonadal mass showed a marginal increase with age in males and no change in females. The demonstration of indeterminate growth of the fish and of the lack of gonadal regression with age has important implications for the characterization of brown ghost knifefish as a model of negligible senescence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".