Data from: Evaluating population genomic candidate genes underlying flowering time in Arabidopsis thaliana using T-DNA insertion lines
Bibliographic record
Abstract
Population genomic scans have emerged as a powerful tool to detect regions of the genome that are potential targets of selection. Despite the success of genomic scans in identifying novel lists of loci potentially underlying adaptation, few studies proceed to validate the function of these candidate genes. In this study, we used T-DNA insertion lines to evaluate the effects of 27 candidate genes on flowering time in North American accessions of Arabidopsis thaliana. We compared the flowering time of T-DNA insertion lines which knock-out the function of a candidate gene obtained from population genomic studies to a wildtype under long- and short-day conditions. We also did the same for a collection of randomly chosen genes that had not been identified as candidates. We validated the well-known effect of long-day conditions in accelerating flowering time and found that gene disruption caused by insertional mutagenesis tends to delay flowering. Surprisingly, we found that knockouts in random genes were just as likely to produce significant phenotypic effects as knockouts in candidate genes. T-DNA insertions at a handful of candidate genes that had previously been identified as outlier loci showed significant delays in flowering time under both long and short days, suggesting that they are promising candidates for future investigation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".