Bibliographic record
Abstract
Phylogenies associated with a paper in review in Plant Ecology. There is a zip file of animal trees and one for plant trees. All are in newick format. Description of tree creation: <strong>* Plants</strong> Plant phylogenies were built using Phylomatic (see http://phylodiversity.net/phylomatic; Webb & Donoghue 2004). Phylomatic is an online interface to retrieve a phylogeny based on a user-defined set of plant species taxonomic names. Branch lengths were estimated for the master plant phylogeny using the branch length adjustment algorithm (BLADJ) in the software Phylocom (Webb, Ackerly & Kembel 2008), which fixes a set of nodes in the tree to specified ages and evenly distributes the ages of the remaining nodes. We used node age estimates from Wikström et al. (2001) as incorporated in the “ages” file in the Phylocom installation. The tree file we used to run the bladj command in Phylocom is provided in multiple formats in Appendix B. See the master plant phylogeny in Appendix B and on Figshare.org. We pruned the master phylogeny for each network. <strong>* Pollinators</strong> For pollinator phylogenies for the unpublished Canadian networks we built a master phylogeny of all animal pollinators across all networks in the study in Mesquite v.2.75 (Maddison & Maddison 2011), based on a variety of published phylogenies. We then pruned the master phylogeny for each network. Pollinator phylogenies from Rezende et al. (2007) were built using a variety of sources (see Rezende et al. 2007 for details). No information on branch lengths was available for pollinator phylogenies, so we assumed all branch lengths equaled one time unit. See the master plant phylogeny in Appendix B and on Figshare.org.
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 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 | 0.000 | 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) | 0.074 | 0.001 |
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; both teacher heads agree on what is shown here.
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".