Selection and Interpretation of Diagnostic Tests in Aquaculture Biosecurity
Bibliographic record
Abstract
In biosecurity-related activities in aquaculture, diagnostic tests are commonly used to (1) demonstrate freedom from infection in a facility, (2) screen aquatic animals prior to introduction to the receiving facility, (3) detect infected animals as early as possible during a quarantine period, and (4) confirm suspicious or clinical case(s). The interpretation of test result(s) is indicative of the true infection status at the individual and at the group levels and has direct implications in completing the stepwise process for Effective Veterinary Biosecurity as proposed by the International Aquatic Veterinary Biosecurity Consortium. The confidence regarding a test result depends on the anticipated level of infection in the investigated aquatic animal population and on the diagnostic sensitivity and specificity of the tests. Depending on the testing intended purpose, the test of choice or combination of test may vary and is primarily based on the test diagnostic sensitivity or specificity. Additional strategies for maximizing the chance of a test result to be true are described in the context of each testing activity and targeted unit of interest (i.e., individual fish or group of fish).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".