Bibliographic record
Abstract
The tree of life is one of the most important organizing principles in biology. Updates and revisions are historically derived from improved data capture, increasingly refined models of evolution and expanded taxon sampling. Tracing the changes in the tree of life over the molecular era (1990-present) highlights the evolution of biologists' understanding of life on earth and serves as a foil placing the explosion of available data over this timeframe in context. Using current-day information, we explored the taxonomic growth captured in a tree of life through historic tree reconstruction. Data capture is now facilitating improvements in genome quality rather than expanding deep diversity, as the rate of novel phylum discovery is slowing for bacteria and archaea. Using dissimilarity metrics, the proportion of changes that each historic tree encompasses identified a diminishing influence of additional taxa on high-level topological revisions. No trees recapitulated current hypotheses for deep relationships on the tree of life, reflective of disadvantages associated with high taxon sampling and the divide-and-conquer methodologies required to analyse extremely large datasets. This work clarifies the effect of the interaction between data quality, data quantity and taxonomic diversity on our ability to construct a stable tree of life.This article is part of the discussion meeting issue 'Chance and purpose in the evolution of biospheres'.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".