Bibliographic record
Abstract
Summary A key question in the fields of macroecology and evolution is how rates of evolution vary across gradients, be they ecological (e.g. temperature, rainfall, net primary productivity), geographic (e.g. latitude, elevation), morphological (e.g. body mass), etc. Evolutionary rates across gradients ( evorag 2.0) is a new software package provided as open source in the r language (and available from CRAN ) that tests whether rates of trait evolution vary continuously across such continuous gradients. The approach uses quantitative trait data for a series of sister‐pair contrasts (i.e. sister species or other types of sister taxa) and applies Brownian Motion and Ornstein Uhlenbeck models in which parameter (evolutionary rate and constraint) values vary as a function of discrete variables (e.g. male vs. female) and/or continuous variables (e.g. latitude, temperature, body size). We used simulation to test performance of the models in Evo RAG . Our gradient models accurately estimate parameter values, have very low levels of bias, and low rates of model misspecification. The modelling framework developed here provides great flexibility in designing models that test how rates of evolution vary across gradients. We provide an example where both discrete (songbird vs. suboscine) and continuous (latitude) effects on evolutionary rate in avian song were simultaneously estimated.
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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 | 0.000 |
| 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".