Assessment of the Safety of Aquatic Animal Commodities for International Trade: The OIE Aquatic Animal Health Code
Bibliographic record
Abstract
Trading of aquatic animals and aquatic animal products has become increasingly globalized during the last couple of decades. This commodity trade has increased the risk for the spread of aquatic animal pathogens. The World Organisation for Animal Health (OIE) is recognized as the international standard-setting organization for measures relating to international trade in animals and animal products. In this role, OIE has developed the Aquatic Animal Health Code, which provides health measures to be used by competent authorities of importing and exporting countries to avoid the transfer of agents pathogenic for animals or humans, whilst avoiding unjustified sanitary barriers. An OIE ad hoc group developed criteria for assessing the safety of aquatic animals or aquatic animal products for any purpose from a country, zone or compartment not declared free from a given disease 'X'. The criteria were based on the absence of the pathogenic agent in the traded commodity or inactivation of the pathogenic agent by the commercial processing used to produce the commodity. The group also developed criteria to assess the safety of aquatic animals or aquatic animal products for retail trade for human consumption from potentially infected areas. Such commodities were assessed considering the form and presentation of the product, the expected volume of waste tissues generated by the consumer and the likely presence of viable pathogenic agent in the waste. The ad hoc group applied the criteria to commodities listed in the individual disease chapters of the Aquatic Animal Health Code (2008 edition). Revised lists of commodities for which no additional measures should be required by the importing countries regardless of the status for disease X of the exporting country were developed and adopted by the OIE World Assembly of Delegates in May 2011. The rationale of the criteria and their application will be explained and demonstrated using examples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".