Mass balance model of Lake Volta fisheries: The use of Ecopath model
Bibliographic record
Abstract
Abstract A mass balance model of trophic interactions among ten key functional producer and consumer groups in Lake Volta was constructed using the Ecopath model to study the energy flows and species interactions in the lake. The present study was based on secondary and primary data on fish catch, diet composition, phytoplankton and zooplankton biomasses, collected in 2015 and 2016. Additional information on growth parameters of major species required for balancing the Ecopath model was obtained from sampling and FishBase. The functional groups were detritus, phytoplankton, zooplankton, benthos, prey fish, Tilapia, Bagrus, Chrysichthys, Alestes and Synodontis species. Four trophic levels were identified in the Lake Volta ecosystem, with the energy flow occurring mainly within the first three trophic levels. The calculated ecotrophic efficiency value of the primary producers (phytoplankton: 0.17; detritus: 0.22) indicated they were least exploited, compared to the secondary producers, zooplankton (0.80) and benthos (0.50). All secondary consumers had ecotrophic efficiency values higher than the primary producers, indicating they are exploited in the ecosystem. The main energy flows in the lake were from phytoplankton and detritus at trophic level I, and Bagrus species, the top predator, at a level of 3.30. The network analysis, illustrating a connectance index of 0.43 and an omnivory index of 0.06, in the lake system indicated the ecosystem is unstable, somewhat immature and still in a developing stage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".