A roadmap for global synthesis of the plant tree of life
Bibliographic record
Abstract
Providing science and society with an integrated, up-to-date, high quality, open, reproducible and sustainable plant tree of life would be a huge service that is now coming within reach. However, synthesizing the growing body of DNA sequence data in the public domain and disseminating the trees to a diverse audience are often not straightforward due to numerous informatics barriers. While big synthetic plant phylogenies are being built, they remain static and become quickly outdated as new data are published and tree-building methods improve. Moreover, the body of existing phylogenetic evidence is hard to navigate and access for non-experts. We propose that our community of botanists, tree builders, and informaticians should converge on a modular framework for data integration and phylogenetic analysis, allowing easy collaboration, updating, data sourcing and flexible analyses. With support from major institutions, this pipeline should be re-run at regular intervals, storing trees and their metadata long-term. Providing the trees to a diverse global audience through user-friendly front ends and application development interfaces should also be a priority. Interactive interfaces could be used to solicit user feedback and thus improve data quality and to coordinate the generation of new data. We conclude by outlining a number of steps that we suggest the scientific community should take to achieve global phylogenetic synthesis.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".