Application of comparative genomics to narrow-leafed lupin (<i>Lupinus angustifolius</i>L.) using sequence information from soybean and<i>Arabidopsis</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The completion of genome-sequencing initiatives for model plants and EST databases for major crop species provides a large resource for gaining fundamental knowledge of complex gene interactions and the functional significance of proteins. There are increasingly numerous opportunities to transfer this information to other plant species with uncharacterized genomes and make advances in genome analysis, gene expression, and predicted protein function. In this study, we have used DNA sequences from soybean and Arabidopsis to determine the feasibility of applying comparative genomics to narrow-leafed lupin. We have used transcribed sequences from soybean and showed that a high proportion cross hybridize to lupin DNA, identifying similar genes and providing landmarks for estimating the degree of chromosomal synteny between species. To further investigate comparative relationships in this study, a detailed analysis of three lupin genes and comparison of orthologs from soybean and Arabidopsis shows that, in some cases, gene structure and expression are highly conserved and their proteins may have similar function. In other cases, genes show variation in expression profiles indicating alternative functions across species. The advantages and limitation of using soybean and Arabidopsis sequences for comparative genomics in lupins are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it