Aquarium fish exploitation in western Amazonia: conservation issues in Peru
Bibliographic record
Abstract
The Amazon basin is a key supplier of wild freshwater fishes to the multi-billion US$ global aquarium market, yet limited information exists on the organization of the regional trade, its importance to local economies or conservation impacts. Through field interviews and review of government statistics, this paper describes the state of the industry in Peru, reporting on the scale and value of the trade, the nature of the fishery and the characteristics and roles of key actors in regional supply networks. An economically important industry is revealed, with 28 firms officially exporting over nine million fishes worth US$ 2.5 million to 24 countries in 2001, and involving fish species from 36 families transported from rainforest catchments up to 1100 km distant from the export centre of Iquitos. Most fish are however collected close to the city, with 10 species representing >70% of trade volume. Some 10 000 people earn income from the trade, among them many rural poor who depend on aquarium fish collection as a primary or supplementary source of cash income. The industry is currently undergoing an important transition towards supplying new Asian and European markets, increasing exports of species biologically unsuited to heavy exploitation as a result, and highlighting the conservation need for improved knowledge and management of the trade in Amazonia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".