Soil carbon and nitrogen responses to snow removal and concrete frost in a northern coniferous forest
Bibliographic record
Abstract
Climate change in northeastern North America is resulting in warmer winters with reduced snow accumulation. Soils under a thin snowpack are more likely to experience freeze–thaw cycles, disrupting carbon (C) and nitrogen (N) transformations. We conducted a 2 year snow removal experiment in Maine to study the effects of soil freezing on soil C and N processes. O horizon soils were sampled during winter and spring of 2015 and 2016, and they were analyzed for labile inorganic N and water-extractable organic carbon (WEOC) concentrations, specific ultraviolet absorbance (SUVA254), and potential net N mineralization. The winter of 2015 was cold and snowy, whereas 2016 was warm with a shallow, short-term snowpack. Snow removal caused the soils to freeze, but winter rain-on-soil events in 2015 resulted in the formation of concrete frost, as opposed to granular frost in 2016. Concrete frost increased soil ammonium (NH4+-N) and WEOC concentrations and decreased SUVA254, which we attribute to microbial cell lysis. In contrast, granular frost did not alter soil nutrient concentrations, reflecting limited microbial distress. Our study demonstrates that moisture content influences the intensity of soil freezing, highlighting the importance of snowpack depth and winter rain events in regulating winter and spring biogeochemical processes and nutrient availability.
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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".