A machine learning approach to study plant functional trait divergence
Bibliographic record
Abstract
Premise: Plant functional traits are often used to describe the spectra of ecological strategies used by different species. Here, we demonstrate a machine learning approach for identifying the traits that contribute most to interspecific phenotypic divergence in a multivariate trait space. Methods: , including random forest and gradient boosting machine classifiers and recursive feature elimination. These approaches were applied at the genus level as well as within each of the three major clades within the genus to examine the variability in the major axes of trait divergence in three independent species radiations. Results: Machine learning models were able to predict species identity from functional traits with high accuracy, and differences in functional trait importance were observed between the genus and clade levels indicating different axes of phenotypic divergence. Conclusions: Applying machine learning approaches to identify divergent traits can provide insights into the predictability or repeatability of evolution through the comparison of parallel diversifications of clades within a genus. These approaches can be implemented in a range of contexts across basic and applied plant science from interspecific divergence to intraspecific variation across time, space, and environmental conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".