Code and files for : Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale. Kelly et al. (2021) Ecology Letters
Bibliographic record
Abstract
Code and data files for : Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale, Ecology Letters, 2021, in press.. By: Ruth Kelly, Kevin Healy, Madhur Anand, Maude Baudraz, Michael Bahn, Bruno E. L. Cerabolini, Johannes H. C. Cornelissen, John M. Dwyer, Andrew L. Jackson, Jens Kattge, Ülo Niinemets, Josep Penuelas, Simon Pierce, Roberto Salguero-Gómez, & Yvonne M. Buckley Code and analysis are described in detail in the main text and supplementary materials of the associated Ecology Letters paper. If you have any questions regarding the R code files you may contact Ruth Kelly at kellyr44@tcd.ie or ruth.kelly@afbini.gov.uk. Data provided herein, represent a derived compilation from multiple data sources, and are primarily provided for transparency and reproducibility of our analyses. The databases we took our original data from are constantly growing and being updated. Therefore, if you want to do further more detailed research in this area we refer you to the following data sources which were invaluable to us. Please cite these original data sources alongside our Ecology Letters paper if you use our compiled dataset in any publication. Further references to trait data used in the compilation of our dataset are given in supplementary information accompanying the main paper. Life history COMPADRE - https://compadre-db.org/ Salguero-Gomez, R., Jones, O.R., Archer, C.R., Buckley, Y.M., Salguero-g, R., Che-castaldo, J., et al. (2015). The COMPADRE Plant Matrix Database : an open online repository for plant demography. J. Ecol., 202–218. Traits TRY - https://www.try-db.org/ Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I.C., Leadley, P., et al. (2020). TRY plant trait database – enhanced coverage and open access. Glob. Chang. Biol., 26, 119–188. Bien - https://bien.nceas.ucsb.edu/bien/ Enquist, B., Condit, R., Peet, R., Schildhauer, M. & Thiers, B. (2016). Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ Prepr., e2615v2. Climate http://www.csi.cgiar.org Trabucco, A. & Zomer, R.J. (2009). Global Aridity Index (Global-Aridity) and Global Potential Evapo-Transpiration (Global-PET) Geospatial Database. CGIAR Consort. Spat. Information. http://www.worldclim.org Fick, S.E. & Hijmans, R.J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol., 37, 4302–4315. The phylogeny used here is an edited subset of: Zanne, Amy E. et al. (2014), Data from: Three keys to the radiation of angiosperms into freezing environments, Dryad, Dataset, https://doi.org/10.5061/dryad.63q27
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.355 | 0.379 |
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".