The LN54 Radiation Hybrid Map of Zebrafish Expressed Sequences
Bibliographic record
Abstract
To increase the density of a gene map of the zebrafish, Danio rerio, we have placed 3119 expressed sequence tags (ESTs) and cDNA sequences on the LN54 radiation hybrid (RH) panel. The ESTs and genes mapped here join 748 SSLp markers and 459 previously mapped genes and ESTs, bringing the total number of markers on the LN54 RH panel to 4226. Addition of these new markers brings the total LN54 map size to 14,372 cR, with 118 kb/cR. The distribution of ESTs according to linkage groups shows relatively little variation (minimum, 73; maximum, 201). This observation, combined with a relatively uniform size for zebrafish chromosomes, as previously indicated by karyotyping, indicates that there are no especially gene-rich or gene-poor chromosomes in this species. We developed an algorithm to provide a semiautomatic method for the selection of additional framework markers for the LN54 map. This algorithm increased the total number of framework markers to 1150 and permitted the mapping of a high percentage of sequences that could not be placed on a previous version of the LN54 map. The increased concentration of expressed sequences on the LN54 map of the zebrafish genome will facilitate the molecular characterization of mutations in this species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 0.002 |
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".