Identification of ISSR markers associated with productivity traits in silkworm,<i>Bombyx mori</i>L.
Bibliographic record
Abstract
Bombyx mori L., commonly recognised around the world as the mulberry silkworm, is characterized by a wide variability in yield and developmental traits, which have been proven through conventional genetic analysis to be of polygenic nature. A large number of morpho-biochemical traits and RFLP and RAPD markers are mapped on different linkage groups, but to this point very little attention has been given to unravelling the genetics of yield traits. To address this issue, polymorphic profiles of 147 markers generated with 12 ISSR primers on the genomic DNA of 20 silkworm stocks of diverse yield status were subjected to multiple regression and discriminant function analyses (DFA). This led to the identification of eight markers generated by six primers, which demonstrated high beta-coefficient indices of -0.451 to -0.940. Furthermore, a significant difference between the yield traits for stocks with and without the specific marker could also be established. The inheritance pattern of one marker, L13800bp, identified at the first step of selection of markers through stepwise regression analyses for five yield parameters is discussed in the context of applying multiple regression analysis for establishing association, if not linkage, between a group of DNA markers and a particular yield trait of polygenic nature and using such markers in molecular marker-assisted breeding programs.
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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 | 0.000 |
| 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".