Environmental and plant community composition and functional trait data across peatlands of the Forest Dynamics Plot at Scotty Creek, NT.
Bibliographic record
Abstract
This is the final dataset associated with the publication, “Permafrost condition determines plant community composition and community-level foliar functional traits in a boreal peatland”, submitted to Ecology and Evolution in April 2021. Included are three data files: the plant community composition dataset, the associated environmental variables dataset, and the plant functional trait dataset. Combined, the community composition and trait datasets were used to calculate the community-weighted means presented in the manuscript. These data explored how plant community composition and traits change across the Scotty Creek Forest Dynamics plot in response to environmental variation, including active layer thickness, organic layer thickness, and forest structure (i.e., canopy cover and tree basal area). To do this, we used a random stratified design to select ten 20 m by 20 m grid cells belonging to each of four aboveground tree biomass categories for a total of 40 grid cells across the Scotty Creek Forest Dynamics Plot. Within each grid cell, we randomly placed two 1 m by 1m quadrats to assess community composition of vascular plants via stem counts and measure canopy cover, active and organic layer thickness. These data were averaged to provide an estimate at the grid cell level. Basal area was calculated at the level of the grid cell. Plant functional traits were collected from 3 replicate individuals per species within 2-3 grid cells per aboveground tree biomass category. See Standen and Baltzer (2021) for more detailed methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".