A universal chromosome identification system for maize and wild <i>Zea</i> species
Bibliographic record
Abstract
Abstract Maize was one of the first eukaryotic species in which individual chromosomes can be identified cytologically, which made maize one of the oldest models for genetics and cytogenetics research. Nevertheless, consistent identification of all 10 chromosomes from different maize lines as well as from wild Zea species remains a challenge. We developed a new technique for maize chromosome identification based on fluorescence in situ hybridization (FISH). We developed two oligonucleotide-based probes that hybridize to 24 chromosomal regions. Individual maize chromosomes show distinct FISH signal patterns, which allow universal identification of all chromosomes from different Zea species. We developed karyotypes from three Zea mays subspecies and two additional wild Zea species based on individually identified chromosomes. A paracentric inversion was discovered on the long arm of chromosome 4 in Z. nicaraguensis and Z. luxurians based on modifications of the FISH signal patterns. Chromosomes from these two species also showed distinct distribution patterns of terminal knobs compared to other Zea species. These results support that Z. nicaraguensis and Z. luxurians are closely related 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".