Genome Mapping Nomenclature
Bibliographic record
Abstract
BACKGROUND: Genome Mapping Technologies (optical and electronic) use ultra-high molecular weight DNA to detect structural variation and have application in constitutional genetic disorders, hematological neoplasms, and solid tumors. Genome mapping can detect balanced and unbalanced structural variation, copy number changes, and haplotypes. The technique is analogous to chromosomal microarray analysis, although genome mapping has the added benefit of being able to detect and ascertain the nature of more abnormalities in a single assay than array, karyotyping, or FISH alone. KEY MESSAGES: This paper describes a specific nomenclature for genome mapping that can be used by diagnostic and research centers to report their findings accurately. An international nomenclature is essential for patient results to be understood by different healthcare providers as well as for clear communication in publications and consistency in databases. SUMMARY: Genome mapping can detect aneuploidy, balanced and unbalanced structural variation, as well as copy number changes. The Standing Committee for the International System for Human Cytogenomic Nomenclature (ISCN) recognised there was a need for a specific nomenclature for genome mapping that encompasses the range of abnormalities detected by this technique. This paper explains the general principles of the nomenclature as well as giving specific ISCN examples for the different types of numerical and structural rearrangements.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".