Effect of Pollution on Length-Weight Relationship and Condition Factor of Ethmalosa fimbriata and Chrysichthys macropogon in Ilaje LGA, Ondo State, Nigeria
Bibliographic record
Abstract
Length–weight relationship (LWR) and condition factor (K) are critical parameters for evaluating fish growth patterns, stock status, and ecosystem health. This study examined Ethmalosa fimbriata and Chrysichthys macropogon from four coastal fishing villages; Ayetoro, Bijimi, Idiogba, and Asumogha in Ilaje Local Government Area, Ondo State, Nigeria. A total of 320 specimens were collected between April and July using gillnets of varying mesh sizes. Standard length and body weight were measured, and LWR parameters were estimated using log-transformed regressions, while Fulton’s condition factor was applied to assess fish health and habitat suitability. Results showed that both species exhibited allometric growth, with growth exponent (b) values significantly deviating from the isometric standard of 3. The condition factor for E. fimbriata ranged from 0.92 at Bijimi to 1.56 at Idiogba, while C. macropogon varied from 0.74 at Asumogha to 1.70 at Ayetoro. Higher K values at Idiogba and Ayetoro indicate relatively favorable habitats, whereas lower values at Bijimi and Asumogha suggest environmental stress and reduced food availability. Correlation analysis revealed a positive but site-dependent relationship between length and weight, with stronger associations in stations of higher habitat quality. These findings underscore the influence of habitat variability on fish condition and highlight the need for continuous ecological monitoring. The study provides a baseline for sustainable fisheries management and conservation strategies in Nigeria’s coastal waters.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".