Construction of a high-density, high-resolution genetic map and its integration with BAC-based physical map in channel catfish
Bibliographic record
Abstract
Construction of genetic linkage map is essential for genetic and genomic studies. Recent advances in sequencing and genotyping technologies made it possible to generate high-density and high-resolution genetic linkage maps, especially for the organisms lacking extensive genomic resources. In the present work, we constructed a high-density and high-resolution genetic map for channel catfish with three large resource families genotyped using the catfish 250K single-nucleotide polymorphism (SNP) array. A total of 54,342 SNPs were placed on the linkage map, which to our knowledge had the highest marker density among aquaculture species. The estimated genetic size was 3,505.4 cM with a resolution of 0.22 cM for sex-averaged genetic map. The sex-specific linkage maps spanned a total of 4,495.1 cM in females and 2,593.7 cM in males, presenting a ratio of 1.7 : 1 between female and male in recombination fraction. After integration with the previously established physical map, over 87% of physical map contigs were anchored to the linkage groups that covered a physical length of 867 Mb, accounting for ∼90% of the catfish genome. The integrated map provides a valuable tool for validating and improving the catfish whole-genome assembly and facilitates fine-scale QTL mapping and positional cloning of genes responsible for economically important traits.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".