Replication Data for: Increased snow and cold season temperatures alter High Arctic parasitic fungi - host plant interactions.
Bibliographic record
Abstract
Dataset for Moriana-Armendariz et al. 2021- Increased snow and cold season temperatures alter High Arctic parasitic fungi - host plant interactions. In the Arctic, fungal mycelial growth takes place mainly during the cold-season and beginning of growing season. Climate change induced increases of cold-season temperatures may, hence, benefit fungal growth and increase their abundance. This is of special importance for parasitic fungi, which may significantly shape Arctic vegetation composition. Here, we studied two contrasting plant parasitic fungi’s occurrences (biotrophic Exobasidium hypogenum on vascular plant Cassiope tetragona, and necrotrophic Pythium polare on moss Sanionia uncinata) in response to increased snow depth, a method primarily used to increase cold-season temperatures, after 7-13 years of snow manipulation in Adventdalen, Svalbard. We show that enhanced snow depth increased occurrences of both fungi tested here, and indicate that increased fungal infections of host plants were at least partly responsible for decreases of host occurrences. While bryophyte growth in general may be influenced by increased soil moisture and reduced competition from vascular plants, Pythium is likely enhanced by the combination of milder winter temperatures and moister environment provided by the snow. The relationships between host plants and fungal infection indicate ongoing processes involved in the dynamics of compositional adjustment to changing 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.162 | 0.058 |
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".