Bibliographic record
Abstract
Freshwater fisheries were an important component of the natural and cultural heritage of the former USSR and its republics. Six articles in this issue, “A Rare Glimpse of the Freshwater Fishes of Central Asia,” provide unique insights into the freshwater fish fauna and fisheries of Dagestan, Kazakhstan, Kyrgyzstan, Lake Baikal, Sea of Azov, and the Ukraine (Figure 1).To the English-speaking world, the fisheries of Lake Baikal are perhaps the best known, those of the Ukraine and the Sea of Azov lesser known, and those of Dagestan, Kazakhstan, and Kyrgyzstan virtually unknown. These articles, written by authorities on the freshwater fisheries of these regions, provide an invaluable wealth of information never before published in English, not even in the English version of Voprosy Ikhtiologii, the Russian Journal of Ichthyology. The articles summarize the freshwater fish fauna and history of fisheries in these regions and challenges faced including overexploitation, environmental degradation, and invasive species - threats common the world over. They also identify challenges, and offer solutions, related to the need for collaborative inter-jurisdictional management of shared waterbodies, modernization of commercial fishing and aquaculture, modernization and enforcement of fisheries regulations, and increased capacity to conduct research and undertake management.This work is part of the continuing series of compendia of papers published by the Aquatic Ecosystem Health and Management Society (AEHMS) on fish faunas and fisheries of the world (Aquatic Ecosystem Health and Management [AEHM], 2001, 2006, 2007a,b, 2010, 2013). The Society is once again extremely pleased to bring together another set of articles that provide a rare glimpse of the freshwater fishes of some of the least known areas in the world – those of Central Asia.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.738 | 0.698 |
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".