Tilapia Lake Virus (TiLV): a Globally Emerging Threat to Tilapia Aquaculture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tilapia lake virus is a globally emerging virus responsible for episodes of mass mortality in cultured and/or feral tilapia (Oreochromis spp. and hybrids) in Asia, Africa, Central America, and South America. Since 2014, there have been global reports of TiLV disease resulting in 10% to 90% mortality in tilapia fry, juveniles, and adults causing significant economic losses. Currently, the disease has been confirmed in Colombia, Ecuador, Egypt, India, Indonesia, Israel, Malaysia, Mexico, Philippines, Peru, Tanzania, and Thailand. TiLV has not yet been found in the USA or Canada, but it has most recently been reported in 20 aquaculture production facilities across six Mexican states (Chiapas, Jalisco, Michoacán, Sinaloa, Tabasco and Veracruz). This 7-page fact sheet written by Lowia Al-Hussinee, Kuttichantran Subramaniam, Win Surachetpong, Vsevolod Popov, Kathleen Hartman, Katharine Starzel, Roy Yanong, Craig Watson, Hugh Ferguson, Salvatore Frasca Jr., and Thomas Waltzek and published by the UF/IFAS School of Forest Resources and Conservation, Program in Fisheries and Aquatic Sciences describes this important emerging disease and explains how to prevent outbreaks and what to do if you suspect TiLV in an aquaculture facility or in the wild. http://edis.ifas.ufl.edu/fa213
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.031 |
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 it