Study of fish exploitation pattern of lake Gerio, Yola, Adamawa State, Nigeria
Bibliographic record
Abstract
Lack of exploratory data for effective stock management, negligence on the part of the regulatory bodies with regards to data collection have made clear impossible to know precisely how much fish is taken from lake Geriyo daily, monthly, or annually and this has led to decline in the fisheries yield and reduction in the livelihood of the fishermen in this community. An investigation was carried out on Lake Geriyo between the months of April and July 2010 to know the pattern of fish exploitation. A total of fourteen (14) species of thirteen (13) families were identified during the period. The family Claridae is the only family with two species while the other families had only one specie. Mormyrus species were found to have the highest percentage (27.7%) by number while Clarias species had the lowest of 7.54%. The total numbers of species were 1140 species and the corresponding total weights were 198 kg. In the course of the study, fishermen used unregulated mesh sized less than 2 inches. And most of the fish caught were undersized ranging from 0.06 kg to 0.78 kg. The mean catch per unit effort is 3.67. The correlation coefficient, r=0.734 which shows that there is a high correlation between the effort and the yield. The Yield (y)=11.143+3.560 effort which is an evidence of 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 | 0.001 | 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".