Seasonal and annual dynamics of western Canadian boreal forest plant communities: A legacy data set spanning four decades
Bibliographic record
Abstract
As boreal forests rapidly warm due to anthropogenic climate change, long-term baseline community data are needed to effectively characterize the corresponding ecological changes that are occurring in these forests. The combined seasonal dynamics (SEADYN) and annual dynamics (ANNDYN) data set, which documents the vegetative changes in boreal forests during the snow-free period, is one such source of baseline community data. These data were collected by George H. La Roi and colleagues in Alberta, Canada from 1980 to 2015 within permanent sampling plots established in the Hondo-Slave Lake area (eight stands; 1980-2015) in central Alberta and the Athabasca Oil Sands (AOS) region (17 stands; 1981-1984) near Fort McMurray in northeastern Alberta. Various data were collected, with temporal and spatial coverage differing by data set. These data sets include, but are not limited to, cover of each identified vascular plant and bryoid (moss, liverwort, and lichen) species; forest mensuration; forest litter production; and soil temperature and moisture. Notably, permanent sampling plots were set up as a grid, which will facilitate analyses of spatial relations. These data can be used to analyze long-term changes in seasonal dynamics and succession within boreal forest communities and serve as a baseline for comparison with future forest conditions in unmanaged, managed, and reclaimed forests. Data are released under a CC-BY license; please cite this data paper when using the data for analyses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".