Efficiency of RFLP, RAPD, and AFLP markers for the construction of an intraspecific map of the tomato genome
Bibliographic record
Abstract
We have constructed a tomato genetic linkage map based on an intraspecific cross between two inbred lines of Lycopersicon esculentum and L. esculentum var. cerasiforme. The segregating population was composed of 153 recombinant inbred lines. This map is comprised of one morphological, 132 RFLP (restriction fragment length polymorphism, including 16 known-function genes), 33 RAPD (random amplified polymorphic DNA), and 211 AFLP (amplified fragment length polymorphism) loci. We compared the 3 types of markers for their polymorphism, segregation, and distribution over the genome. RFLP, RAPD, and AFLP methods revealed 8.7%, 15.8%, and 14.5% informative bands, respectively. This corresponded to polymorphism in 30% of RFLP probes, 32% of RAPD primers, and 100% of AFLP primer combinations. Less deviation from the 1:1 expected ratio was obtained with RFLP than with AFLP loci (8% and 18%, respectively). RAPD and AFLP markers were not randomly distributed over the genome. Most of them (60% and 80%, respectively) were grouped in clusters located around putative centromeric regions. This intraspecific map spans 965 cM with an average distance of 8.3 cM between markers (of the framework map). It was compared to other published interspecific maps of tomato. Despite the intraspecific origin of this map, it did not show any increase in length when compared to the high-density interspecific map of tomato.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".