Market Delineation Study of the Fish Market in Nigeria: An Application of Cointegration Analysis
Bibliographic record
Abstract
In Nigeria aquaculture has provided an avenue to bridge the ever-widening demand and supply gap in fish. Ituses land resources that would have otherwise been a waste. This paper examined whether Catfish is in the samemarket with Hake, Mackerel and Sadinnela. Unit root tests, Johansen`s bivariate and multivariate co integrationanalyses were carried out. The analyses show that there is co integration among the species.. The hypothesis ofno substitution between Catfish and the imported species was rejected All the species were classified as being inthe same market and are close substitutes. The results indicate that the price of catfish is not insulated from theprices of the imported species. The prices of the imported species are however insulated from the price of thelocal species. Fish production policies designed to alter fish prices without taking into account the foreign pricesare not likely to be effective in Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".