Conifer genomics and adaptation: at the crossroads of genetic diversity and genome function
Bibliographic record
Abstract
Summary Conifers have been understudied at the genomic level despite their worldwide ecological and economic importance but the situation is rapidly changing with the development of next generation sequencing (NGS) technologies. With NGS, genomics research has simultaneously gained in speed, magnitude and scope. In just a few years, genomes of 20–24 gigabases have been sequenced for several conifers, with several others expected in the near future. Biological insights have resulted from recent sequencing initiatives as well as genetic mapping, gene expression profiling and gene discovery research over nearly two decades. We review the knowledge arising from conifer genomics research emphasizing genome evolution and the genomic basis of adaptation, and outline emerging questions and knowledge gaps. We discuss future directions in three areas with potential inputs from NGS technologies: the evolutionary impacts of adaptation in conifers based on the adaptation‐by‐speciation model; the contributions of genetic variability of gene expression in adaptation; and the development of a broader understanding of genetic diversity and its impacts on genome function. These research directions promise to sustain research aimed at addressing the emerging challenges of adaptation that face conifer trees. Contents Summary 44 I. Introduction 44 II. Why conifer genomics? 45 III. Conifer genome structure and evolution 46 IV. Genomics of adaptation in conifers 49 V. Emerging research directions toward a more integrated understanding of genetic diversity and genome function 57 VI. Conclusion 58 Acknowledgements 58 References 58
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".