Vascular plant community surveys across different reindeer grazing regimes in the Fennoscandian tundra
Bibliographic record
Abstract
This dataset contains data from the experiment described in "Gibson K., Olofsson, J. Mooers, A. Ø., & Monroe, M. J. (2021) Pulse grazing by reindeer (Rangifer tarandus) can increase the phylogenetic diversity of vascular plant communities in the Fennoscandian tundra. Ecology and Evolution. In press." The data is from a multi-year (2004-2007) quasi-experimental study in Northern Fennoscandia, which was designed to analyze the effect of reindeer grazing on vascular plant community diversity. Our study design used a permanent fence constructed in the 1960s and temporary fences constructed along the permanent fence to expose plant communities to three different grazing regimes: light (almost never grazed), pulse (grazed every other year) and press (chronic grazing for over forty years). The study deisgn consisted of plots setup at five different sites at least 100m apart from each other along the permanent fence. Each site was divided into the three grazing regimes (light, pulse and press). For each site and grazing regime, one replicate plot was placed in a drier area and other in a wetter area. Each plot (n = 36) was evenly split into nine subplots. This dataset is composed of plant survey data at the sub-plot level with a variable for the presence/absence and relative abundance of each surveyed species. The biodiversity metrics used in the associated manuscript can be calculated from these variables. The main results of this experiment were that (1) the species richness and evenness of plant communities with pulse and press grazing did not differ from communities with light grazing, (2) there was a transition from shrub‐dominated communities with light grazing to graminoid‐dominated communities with pulse and press grazing and (3) communities with pulse, but not press, grazing were more phylogenetically dispersed than communities with light grazing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".