Artificially designed hybrids facilitate efficient generation of high-resolution linkage maps
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
When sequencing eukaryotic genomes, linkage maps are indispensable for building scaffolds to assemble and/or to validate chromosomes. However, current approaches to constructing linkage maps are limited by marker density and cost-effectiveness, especially for wild organisms. We have now devised a new strategy based on artificially generated hybrid organisms to acquire ultrahigh-density genomic markers at reduced cost and build highly accurate linkage maps. We have also developed the novel analysis pipeline Scaffold Extender with Low Depth Linkage Analysis (SELDLA) for data processing to generate linkage maps and draft genomes. Using SELDLA, linkage maps and improved genomes for two species of pufferfish, Takifugu rubripes and Takifugu stictonotus, were obtained simultaneously. The strategy is applicable to a wide range of sexually reproducing organisms, and could, therefore, accelerate the whole genome analysis of various organisms including fish, mollusks, amphibians, insects, plants, and even mammals.
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 | 0.001 | 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 | 0.000 | 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 it