Vegetation responses to 26 years of warming at Latnjajaure Field Station, northern Sweden
Bibliographic record
Abstract
Climate change is rapidly warming high latitude and high elevation regions influencing plant community composition. Changes in vegetation composition have motivated the coordination of ecological monitoring networks across the Arctic, including the International Tundra Experiment. We have established a long-term passive warming experiment using open-top chambers, which includes five distinct plant communities (Dry Heath; Tussock Tundra; and Dry, Mesic, and Wet Meadow). We measured changes in plant community composition based on relative abundance differences over 26 years. In addition, relative abundance changes in response to fertilization and warming treatments were analyzed based on a seven-year Community-Level Interaction Program experiment. The communities had distinct soil moisture conditions, leading to community-specific responses of the plant growth forms (deciduous shrubs, evergreen shrubs, forbs, and graminoids). Warming significantly affected growth forms, but the direction of the response was not consistent across the communities. Evidence of shrub expansion was found in nearly all communities, with soil moisture determining whether it was driven by deciduous or evergreen shrubs. Graminoids increased in relative abundance in the Dry Meadow due to warming. Growth form responses to warming are likely mediated by edaphic characteristics of the communities and their interactions with climate.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".