Bibliographic record
Abstract
Leaf and root traits and abundances and phylogeny for 76 grassland plant species from Alberta, Canada. Data set and collection methods are described in: S.W. Kembel and J.F. Cahill, Jr. 2011. Independent evolution of leaf and root traits within and among temperate grassland plant communities. PLoS ONE 6(6): e19992. File descriptions community.traits.csv - Each row represents abundance and traits measured for a species in a 20x20m sample plot. Traits should be log10-transformed prior to analysis. There are a few missing data points. Traits were measured on mature healthy leaves and on fine roots (<2mm diameter) taken from representative individuals in each plot. -abund: the abundance of each species in the plot. Abundance is an estimate of the percent cover of the species in the plot (0-100%) to the nearest 10%. Abundance was measured by noting presence of species in 10 quadrats placed within the plot. -SLA: specific leaf area -LeafArea: one-sided projected leaf area -LeafThickness: leaf laminar thickness -LeafTissueDens: leaf tissue density -SRL: specific root length -RootTissueDens: root tissue density -RootDiam: average root diameter plot.metadata.csv - Plot-level metadata on habitat and site plus plot aspect/slope. species.phylogeny.txt - Species-level phylogeny with branch lengths proportional to estimated clade age. Tree backbone based on Davies et al. angiosperm phylogeny, within-family relationships resolved by hand using references cited in Kembel and Cahill 2011.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.103 | 0.041 |
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".