The USA's international trade in fish leather, from a conservation perspective
Bibliographic record
Abstract
This paper provides the first analysis of imports and exports of fish leather by the USA. Estimates of minimum levels of trade were obtained from the records of the United States Fish and Wildlife Service for 1997–2001, and possible conservation consequences were considered. Data show that imported leather items used the skins of at least 51 types of fish. Of the 41 identified to species level, six were freshwater fish, eight diadromous and 27 were fully marine. Eels and hagfishes (marketed as ‘eelskin’; eight named species), stingrays (10 named species) and sharks (15 named species) dominated the trade. An average of 725 000 fish-leather products, worth over US$ 6 million, was imported each year to the USA. A significant decline in fish leather imports over the five-year period studied derived largely from changes in ‘eelskin’ imports. Fish leather in the USA was reportedly sourced primarily from the Republic of Korea, mainland China and Thailand, although the records were flawed. About 93% of leather products were obtained from wild fish. Exports from the USA totalled approximately 5% of imports by volume. Many of the fish species comprising the largest imports for leather were characterized by low resilience to exploitation, with one-third of known species considered threatened or near threatened by the World Conservation Union (IUCN). This pilot assessment indicates the need for better record keeping if sustainability of fish exploitation for leather is to be evaluated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".