Bibliographic record
Abstract
Traditional FISH analysis has employed, at most, two colors of detection, a red-fluorescing fluorochrome and a green-fluorescing fluorochrome. The improvements in fluorescent imaging and development of heartier fluorochromes/dyes have enabled investigators to use several different DNA probes in one experiment. This may involve all or combinations of locus-specific probes and chromosome paints. The value of such experiments lies in the investigator obtaining far more information from one specific cell at one time, rather then carrying out separate experiments on multiple specimens prepared from the same sample, then extrapolating results. The generic term for multi-color FISH assays is M-FISH, however, the technologies behind the manner in which the fluorochrome information is generated has spawned two different M-FISH systems: spectral karyotyping (SKY) and M-FISH. For both assays, the experimental procedures are identical: commercially available probes for all 24 (human) chromosomes are differentially labeled according to a labeling scheme and hybridized to metaphase spreads for 24 to 48 hr, followed by post-hybridization washes and, if required, antibody detection. The difference lies in the imaging: spectral karyotyping identifies the differentiation of the chromosomes based on their spectral properties, whereas M-FISH identifies the differentiation of the chromosomes based on that fluorochrome's presence or absence when visualized with specific filters. The resulting analysis for both methods is the same, revealing hidden translocations and insertions as well as the chromosomal components of marker chromosomes.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".