Bibliographic record
Abstract
Cytogenetics was conceived in the late 1800s and nurtured through the early 1900s by discoveries pointing to the chromosomal basis of inheritance. The relevance of chromosomes to human health and disease was realized more than half a century later when improvements in techniques facilitated unequivocal chromosome delineation. Veterinary cytogenetics has benefited from the information generated in human cytogenetics which, in turn, owes its theoretical and technical advancement to data gathered from plants, insects and laboratory mammals. The scope of this science has moved from the structure and number of chromosomes to molecular cytogenetics for use in research or for diagnostic and prognostic purposes including comparative genomic hybridization arrays, single nucleotide polymorphism array-based karyotyping and automated systems for counting the results of standard FISH preparations. Even though the counterparts to a variety of human diseases and disorders are seen in domestic animals, clinical applications of veterinary cytogenetics will be less well exploited mainly because of the cost-driven nature of demand on diagnosis and treatment which often out-weigh emotional and sentimental attachments. An area where the potential of veterinary cytogenetics will be fully exploited is reproduction since an inherited aberration that impacts on reproductive efficiency can compromise the success achieved over the years in animal breeding. It is gratifying to note that such aberrations can now be tracked and tackled using sophisticated cytogenetic tools already commercially available for RNA expression analysis, chromatin immunoprecipitation, or comparative genomic hybridization using custom-made microarray platforms that allow the construction of microarrays that match veterinary cytogenetic needs, be it for research or for clinical applications. Judging from the technical refinements already accomplished in veterinary cytogenetics since the 1960s, it is clear that the importance of the achievements to date are bound to be matched or out-weighed by what awaits to be accomplished in the not-too-far future.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".