Data for "Bootstrapping outperforms community-weighted approaches for estimating the shapes of phenotypic distributions"
Bibliographic record
Abstract
This repository contains datasets used in the manuscript entitled "Bootstrapping outperforms community-weighted approaches for estimating the shapes of phenotypic distributions" by Maitner et al. For details of these datasets, see https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution. All datasets contain individual (and in some cases, organ-level) trait measurements. The dataset "all_traits_unscaled_RMBL.rds" was compiled by Christine Lamanna, Lindsey L Sloat, Andrew J. Kerkhoff, and Brian J. Enquist, Full details in https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution The dataset "Julies_panama_data.xlsx" was compiled by Julie Messier and collaborators, full details here: https://doi.org/10.1111/j.1461-0248.2010.01476.x The dataset "TreefrogTadpoles.xlsx" was compiled by Nick Rasmussen, full details here: https://www.jstor.org/stable/44082203 The dataset "zooplankton_2019.zip" was compiled by Ewa Merz and Francesco Pomati. For more details, see www.aquascope.ch , https://github.com/mbaityje/plankifier, https://github.com/tooploox/SPCConvert, and https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.182 | 0.166 |
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".