Deepened snow increases late thaw biogeochemical pulses in mesic low arctic tundra
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pulses of plant-available nutrients to the soil solution are expected to occur during the dynamic winter–spring transition in arctic tundra. Our aims were to quantify the magnitude of these potential nutrient pulses, to understand the sensitivity of these pulses to winter conditions, and to characterize and integrate the environmental and biogeochemical dynamics of this period. To test the hypotheses that snow depth, temperature and soil water—and not snow nutrient content—are important controls on winter and spring biogeochemistry, we sampled soil from under ambient and deepened snow every 3 days from late winter to spring, in addition to the snowpack at the start of thaw. Soil and microbial biogeochemical dynamics were divided into distinct phases that correlated with steps in soil temperature and soil water. Soil solution and microbial pools of C, N and P fluctuated with strong peaks and declines throughout the thaw, especially under deepened snow. Snowpack nutrient accumulation was negligible relative to these biogeochemical peaks. All nutrient and microbial peaks declined simultaneously at the end of snowmelt and so this decline was delayed by 15 days under deepened snow. The timing of these nutrient pulses is critical for plant species nutrient availability and landscape nutrient budgets. This detailed and statistically-based characterisation of the winter–spring transition in terms of environmental and biogeochemical variables should provide a useful foundation for future biogeochemical process-based studies of thaw, and indicate that spring thaw and possibly growing season biogeochemical dynamics are sensitive to present and future variability in winter snow depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.022 | 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 it