Bibliographic record
Abstract
In the Czech Republic, the eel (Anguilla anguilla), was an abundant species for a longtime. Nowadays the eel is threatened species due to the excessive fishing and the loss of migration routes. The individuals from the wild nature belong to one of the main sources of the trade in spite of the fact that the eel is registered on the list of CITES; Appendix II including species which are not instantly threated with extinction (33 % of 309 trades in 5 years). The number and the volume of trades was assessed based on data from CITES. Before including the eel on the CITES list, the registered individuals represented the most significant source of the majority of all trades (55 %). Registered individuals were followed by eels from the wild nature. The biggest part of trades was made by meat (38 %) which is very popular in spite of the high fat content. Living individuals, eel bodies, small leather goods and eel leather were traded also very well. Among the main purposes of the trade belong the commercial market (94 %), scientific and medical purpose as well as the captive breeding. The traded eel came mostly from France (21 % of trades), Korea, Great Britain and Morocco. For 20 % of trades the country of origin was unknown. The most exporting countries were: China (32 %), Mexico, Greece, Morocco and Denmark. Targets export markets were especially in USA (15 %), Hong Kong, Japan, Korea and Canada.
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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 | 0.002 | 0.002 |
| 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) | 0.009 | 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".