Management Indicators and Growth Performance Index of Tilapia Zillii in a Tropical Coastal Estuary
Bibliographic record
Abstract
An investigative study was carried-out on management indicators and growth performance index of T. zillii in a tropical coastal estuary for twenty-four consecutive months using length-weight data. Electronic length frequency analysis (ELEFAN), sub-routine of FiSAT II software and empirical models were used to analyse the data. Results showed that the mean size of T. zillii was between 22.10±1.155 cm and 26.10±0.115 cm in April and January respectively. The highest (349.72±2.725g) and lowest (264.14±4.109g) mean weights were recorded in the respective months of December and April. Exploited sizes ranged from 11-12 cm to 36-37 cm. Length class 24-25 cm was mostly exploited, constituting 9.92% while 36-37 cm length class was the least (0.05 %) on the exploitation data. Length-at-first maturity, Lm, length-at-optimum yield, Lopt, longevity, Tmax, and growth performance index were estimated as 10.23 cm, 23.95 cm, 4 years and 3.076 respectively. It was therefore, recommended that, for the fishery to be sustainable, close monitoring should be given during the months of February and April, and exploitation of larger size fish be regulated to prevent recruitment overfishing.
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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 | 0.000 |
| 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) | 0.000 | 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".