Use and exchange of genetic resources of emerging species for aquaculture and other purposes
Bibliographic record
Abstract
Abstract From a genetic resources viewpoint, emerging aquaculture species and species groups are examined mainly in terms of food use. In addition, we include species that are becoming increasingly important for biodiversity conservation and related ecotourism aspects. Together with ornamental fish species, we argue that these species are facing increasing vulnerability and warrant attention. Our intention is to raise awareness of the potential for increasing production and revenues from emerging species/species groups with an emphasis on an underlying link to biodiversity conservation and ecosystem preservation, and how this information will inform policy on access to the genetic resources and the sharing of benefits derived from their use. For food purposes, the fastest growing aquaculture sector is mariculture, and within this sector groupers and wrasses are considered to be the most important because they cater to the relatively lucrative live food fish restaurant trade (LFFRT), which is rapidly expanding in selected South‐East Asian countries. In the Asian region, ecotourism is an emerging sector and a prominent fish group for this purpose is considered to be mahseer. A number of mahseer species are culturally and commercially important and are often seen as a group of indigenous species that are suitable for aquaculture. This review summarizes much of the limited information related to the patterns of use and exchange of genetic resources on emerging aquatic species/species groups, with particular reference to 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".