Data from: How much of the world is woody?
Bibliographic record
Abstract
Abstract1.The question posed by the title of this paper is a basic one, and it is surprising that the answer is not known. Recently assembled trait datasets provide an opportunity to address this, but scaling these datasets to the global scale is challenging because of sampling bias. Although we currently know the growth form of tens of thousands of species, these data are not a random sample of global diversity; some clades are exhaustively characterised, while others we know little–to–nothing about. 2.Starting with a database of woodiness for 39,313 species of vascular plants (12% of taxonomically resolved species, 59% of which were woody), we estimated the status of the remaining taxonomically resolved species by randomisation. To compare the results of our method to conventional wisdom, we informally surveyed a broad community of biologists. No consensus answer to the question existed, with estimates ranging from 1% to 90% (mean: 31.7%). 3.After accounting for sampling bias, we estimated the proportion of woodiness among the world's vascular plants to be between 45% and 48%. This was much lower than a simple mean of our dataset and much higher than the conventional wisdom. 4.Synthesis: Alongside an understanding of global taxonomic diversity (i.e., number of species globally), building a functional understanding of global diversity is an important emerging research direction. This approach represents a novel way to account for sampling bias in functional trait datasets and to answer basic questions about functional diversity at a global scale
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".