RAPD markers as predictors of infectious hypodermal and hematopoietic necrosis virus (IHHNV) resistance in shrimp (<i>Litopenaeus stylirostris</i>)
Bibliographic record
Abstract
Random amplified polymorphic DNA (RAPD) fingerprints of two shrimp populations (Litopenaeus stylirostris) were compared to find genetic marker(s) that may be associated with infectious hypodermal and hematopoietic necrosis virus (IHHNV) resistance or susceptibility. Of the 100 10-mer random primers and 100 intersimple-sequence repeat (ISSR) primers screened, five provided markers specific to the Super Shrimp population and three provided markers specific to the wild caught population. The two populations were further characterized for relative viral load (reported as cycle threshold, CT) using real-time quantitative PCR with primers specific to the IHHNV genome. The beta-actin gene was amplified to serve as a control for normalization of the IHHNV viral load. The mean viral load was significantly lower (C(T) = 34.58; equivalent to 3.3 x 10(1) copies of IHHNV genome/ng DNA) in Super Shrimp than in the wild caught population (CT = 23.49; equivalent to 4.2 x 10(4) copies/ng DNA; P < 0.001; CT values are inversely related to viral load). A preliminary prediction model was created with Classification and Regression Tree (CART) software (Salford Systems, San Diego, Calif.), where the resultant decision tree uses the presence or absence of seven RAPD markers as predictors of the relative viral load.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".