Effects of soil warming and thermokarst on soil microbial communities in the High Canadian Arctic, 2005-2021.
Bibliographic record
Abstract
Soil microbes are not only key drivers for nutrient and energy cycles, but they are also fast responders to changes in their environment- and as such act as bioindicators of climate change. The temperatures in the Arctic are warming two times faster than at lower latitudes and lead to warming of permafrost soils, an increase of the active layer and eventually to thermokarst, the thawing of permafrost. Thawing permafrost affects the hydrological systems and increases the potential of old carbon becoming available for decomposition. The fate of carbon stores will rest largely on the response of plant and microbial communities to the changed conditions. Especially the ice rich permafrost soils in the High Arctic are highly sensitive to increasing air temperatures, because the permafrost occurs close to the surface and the soils lack the insulation provided by a thick vegetation cover and organic soil horizons as found in the Low Arctic (Farquharson et al. 2019). This study examines the effects of soil warming and thermokarst on fungal and bacterial communities of active layer soils in the three most northern bioclimatic subzones (A, B, C) of the Arctic that occurred over 10 years (2005-2016). We used Illumina sequencing to analyze the fungal and bacterial communities and obtained environmental data to investigate the effect of warming and thermokarst on fungal and bacterial communities in the High Arctic. Here we present the assembled fungal, bacterial and environmental data sets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".