Sequencing and Analyzing the Transcriptomes of a Thousand Species Across the Tree of Life for Green Plants
Bibliographic record
Abstract
The 1,000 Plants (1KP) initiative was the first large-scale effort to collect next-generation sequencing (NGS) data across a phylogenetically representative sampling of species for a major clade of life, in this case the Viridiplantae , or green plants. As an international multidisciplinary consortium, we focused on plant evolution and its practical implications. Among the major outcomes were the inference of a reference species tree for green plants by phylotranscriptomic analysis of low-copy genes, a survey of paleopolyploidy (whole-genome duplications) across the Viridiplantae , the inferred evolutionary histories for many gene families and biological processes, the discovery of novel light-sensitive proteins for optogenetic studies in mammalian neuroscience, and elucidation of the genetic network for a complex trait (C 4 photosynthesis). Altogether, 1KP demonstrated how value can be extracted from a phylodiverse sequencing data set, providing a template for future projects that aim to generate even more data, including complete de novo genomes, across the tree of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".