Geographic and sex‐specific variation in growth of yellow‐eyed mullet, <i>Aldrichetta forsteri,</i> from estuaries around New Zealand
Bibliographic record
Abstract
Abstract Survival and reproductive rates in fish are often a function of body size. Consequently, spatial‐ and sex‐specific variation in somatic growth rates can have important consequences for population growth and resilience. We used otolith‐based approaches to estimate geographic‐ and sex‐specific growth rates of yellow‐eyed mullet ( Aldrichetta forsteri ) collected from 14 estuaries and harbours around New Zealand. Aldrichetta forsteri is an abundant and dominant component of New Zealand's estuarine fish fauna. We extracted otoliths from 511 fish, validated daily and annual increments, and prepared transverse thin sections of otoliths to determine age. “Size‐at‐age” relationships were estimated using both linear‐ and non‐linear (von Bertalanffy) growth models, and model performance was evaluated using Akaike's Information Criterion. Because growth rates of sampled fish were best approximated by linear functions, we used ANCOVA to test the null hypothesis that growth rates of A. forsteri were homogeneous between sexes and among geographic locations around New Zealand. Our analyses suggest heterogeneous growth rates between sexes and among locations. Interestingly, relative growth rates between sexes appeared to vary across separate latitudinal gradients for North Island and South Island. Within each island (but not across islands), female A. forsteri generally grew faster than males at the lowest latitudes; relative growth rates of females declined progressively below males with increasing latitude.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".