Spatial and temporal variations of Limnothrissa miodon stocks and their stability in Lake Kivu
Bibliographic record
Abstract
Limnothrissa miodon is a small pelagic clupeid that was introduced into Lake Kivu in the late 1950s to fill an empty niche. Since then, it has become the main fishery in the lake. The fish stocks were estimated by hydroacoustics between 2012 and 2018 to provide information on the fishery in the current context of changing environmental factors. The main objectives were to determine the most appropriate season for assessing stock dynamics, to characterize temporal and spatial distribution in L. miodon populations and to compare with previous surveys to help predict status of its stocks. The fish size distribution showed that the long dry season was the most appropriate season for assessing the stocks, as it provides information of recruitment for the year. The south and west basins always had higher densities (1.23 m2/ha) and biomass (21–22.7 kg/ha) than the north basins (0.62–0.77 m2/ha; 15.3–16.5 kg/ha). The stock showed declining trend from 7,000 t in 1985 to 1,000 t in 2012 and thereafter consistently increased to 4,000 t in 2018. This last value is similar to previous estimates of 1990s and 2008, showing that the stocks of L. miodon are stable. The low 2012 values could be due to particular environmental conditions in 2012–2014, when there was a shift from diatoms and cyanobacteria to green algae. There is therefore a need to combine high-quality environmental data with fishery surveys to better understand the dynamic of fish and fishery especially under the increasing influence of climate change on lake productivity processes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".