Bibliographic record
Abstract
Freshwater resources are gaining attention as the 21st century unfolds. Human use, and abuse, of freshwater resources are growing almost uncontrollably, with potentially dire consequences for all or most aquatic biota. As a result of concerns expressed by scientists, the conservation and protection of the aquatic biodiversity are gaining worldwide attention. Freshwater fish are among the most threatened taxa globally. Among aquatic biota, they are especially important in their dual role of major human food source and indicator of aquatic ecosystem health.In order to document the situation of the world's freshwater and its resources, the project Freshwater fishes and fisheries of the World: Biodiversity, Health and Habitat was initiated by the Aquatic Ecosystem Health and Management Society (AEHMS). Consequently, the Society planned the preparation of a series of publications to raise awareness of freshwater fishes, health, habitats, biodiversity and fisheries around the world with special attention to the common challenges fish face from human development and anthropogenic activities. In this context, several papers have already been published in various issues of AEHM which originated from the following countries:South America: (Magdalena River, Orinoco River, Amazon River, Paraná River, Paraguay-Parana-Rio de la Plata, Altiplano, Patagonia) PeruCentral America: MexicoOceanica: New ZealandIndian sub-continent: India, Nepal, BangladeshEurope: Greece, Finland, Norway, SwedenFrom this issue we launch our exploration about the freshwater fisheries of the continental Africa. We categorized Africa into seven ecological regions, namely: basins of rivers Nile, Congo, Niger, and Zambezi, and the African Great Lakes (Victoria, Tanganyika and Malawi). The two papers included in this issue deal with Southern Africa:Overview of the Zambezi River System: Its history, fish fauna, fisheries, and conservationLake Malawi: fishes, fisheries, biodiversity, health and habitatWe have been lucky to be able to call upon knowledgeable experts from the area with a great deal of field experience. Additional papers on Africa have been planned and some are under preparation. We will be publishing other articles about African regions as and when they are peer reviewed and processed. We would sincerely like to thank various authors who have been working hard on this project. It is our hope that information available from this series of papers will be successful in revealing a continental picture and in generating an overview of the ‘Freshwater fishes and fisheries of the World: Biodiversity, Health and Habitat.’In addition to the above, we are pleased to include an invited article by N. Mandrak and B. Cudmore. Dr. Nick Mandrak of Fisheries and Oceans Canada is one of the leading experts in the field of invasive species research. His article is timely, thought provoking, and most comprehensive, focusing on an extremely important ecological topic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 0.001 |
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".