BAC‐based upgrading and physical integration of a genetic SNP map in Atlantic salmon
Bibliographic record
Abstract
A better understanding of the genotype-phenotype correlation of Atlantic salmon is of key importance for a whole range of production, life history and conservation biology issues attached to this species. High-density linkage maps integrated with physical maps and covering the complete genome are needed to identify economically important genes and to study the genome architecture. Linkage maps of moderate density and a physical bacterial artificial chromosome (BAC) fingerprint map for the Atlantic salmon have already been generated. Here, we describe a strategy to combine the linkage mapping with the physical integration of newly identified single nucleotide polymorphisms (SNPs). We resequenced 284 BAC-ends by PCR in 14 individuals and detected 180 putative SNPs. After successful validation of 152 sequence variations, genotyping and genetic mapping were performed in eight salmon families comprising 376 individuals. Among these, 110 SNPs were positioned on a previously constructed linkage map containing SNPs derived from expressed sequence tag (EST) sequences. Tracing the SNP markers back to the BACs enabled the integration of the genetic and physical maps by assigning 73 BAC contigs to Atlantic salmon linkage groups.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".