Evolution of the nuclear genome of ferns and lycophytes
Bibliographic record
Abstract
Introduction Analyses of gene expression and function, genetic networks, population polymorphisms, and genome organization at the whole genome level have enabled research on previously intractable questions (reviewed in Wolfe and Li, 2003). Among plant lineages, genomic approaches have been most widely applied in the angiosperms, where significant resources have been developed. Angiosperm studies utilizing genome scale analyses have made several important advances, including the identification of an extensive history of genome duplications (Blanc et al ., 2003; Schlueter et al ., 2004; Cui et al ., 2006), progress in understanding flower development and evolution (Doust et al ., 2005; Whibley et al ., 2006), characterization of the genetics underlying speciation and adaptation (Bradshaw and Schemske, 2003; Rieseberg et al ., 2003; Lai et al ., 2005; Eyre-Walker, 2006), the identification and mapping of recombination hot spots (Drouaud et al ., 2006), and the discovery and role of microRNAs (Bartel and Bartel, 2003; Bartel, 2004). Genomic analyses will undoubtedly continue to provide tests of longstanding questions and offer novel perspectives in biology. For example, modern genomic analyses are capable of explaining the origin of the exceptionally high chromosome numbers of homosporous ferns and lycophytes, a result that will shed light on eukaryotic genome organization and evolution. Although there are rich biological and taxonomic resources for ferns and lycophytes, the genomics of these seed-free plants is still in its infancy, and the tools necessary for genomic studies lag behind those available for seed plants. The first homosporous fern linkage map was published only recently (Nakazato et al ., 2006), whereas a large number of linkage maps for seed plants have accumulated since the 1980s
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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 | 0.000 | 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) | 0.001 | 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 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".