Data from: Warming-induced effects on microbial communities and nitrogen cycling capacity in tundra litter
Bibliographic record
Abstract
Climate warming is changing tundra vegetation in the Arctic, with implications for plant litter properties. To address potential warming effects, we characterized the responses of bacterial and fungal communities and their genetically encoded capacity for inorganic nitrogen-transformations in the litter layer, as well as 15N natural abundance in the underlying soil layer as an integrated measure of nitrogen processes in the soil, in 16 long-term alpine and Arctic tundra warming experiments distributed across 12 circumpolar locations. While local conditions primarily shaped microbial communities, warming indirectly affected microbial nitrogen-cycling potentials through changes in herb dominance and litter mass, resulting in enhanced links between litter nitrogen cycling potentials and δ¹⁵N in the underlying soil. Our results suggest that warming-driven vegetation changes may intensify nitrogen-cycling with possible positive feedback on plant growth and ecosystem respiration across the tundra biome. The data includes 1) Sample metadata, including site information, and litter sample measurements of abiotic properties, abundance of nitrogen cycling genes, and abundance and composition of bacterial and fungal communities (Data Table 1, including "README"), 2) and Plant community data in each sampled plot or OTC at the sites (Data Table 2, including "README"). The 16S rRNA gene and ITS2 sequences of prokaryotic and fungal communities, respectively, are deposited at the NCBI database under the BioProject PRJNA760312 (https://www.ncbi.nlm.nih.gov/bioproject/).
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.019 |
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".