Biological soil crusts influence carbon release responses following rainfall in a temperate desert, northern China
Bibliographic record
Abstract
Abstract How soil cover types and rainfall patterns influence carbon (C) release in temperate desert ecosystems has largely been unexplored. We removed intact crusts down to 10 cm from the Shapotou region, China, and measured them in PVC mesocosms, immediately after rainfall. C release rates were measured in soils with four cover types (moss‐crusted soil, algae‐crusted soil, mixed (composed of moss, algae, and lichen)‐crusted soil, and mobile dune sand). We investigated seven different rainfall magnitudes (0–1, 1–2, 2–5, 5–10, 10–15, 15–20, and >20 mm) under natural conditions. C release from all four BSCs increased with increasing rainfall amount. With a rainfall increase from 0 to 45 mm, carbon release amounts increased from 0.13 ± 0.09 to 15.2 ± 1.35 gC m −2 in moss‐crusted soil, 0.08 ± 0.06 to 6.43 ± 1.23 gC m −2 in algae‐crusted soil, 0.11 ± 0.08 to 8.01 ± 0.51 gC m −2 in mixed‐crusted soil, and 0.06 ± 0.04 to 8.47 ± 0.51 gC m −2 in mobile dune sand, respectively. Immediately following heavy rainfall events (44.9 mm), moss‐crusted soils showed significantly higher carbon release rates than algae‐ and mixed‐crusted soils and mobile dune sands, which were 0.95 ± 0.02, 0.30 ± 0.03, 0.13 ± 0.04, and 0.51 ± 0.02 μmol CO 2 m −2 s −1 , respectively. Changes in rainfall patterns, especially large rain pulses (>10 mm) affect the contributions of different soil cover types to carbon release amounts; moss‐crusted soils sustain higher respiration rates than other biological crusts after short‐term extreme rainfall events.
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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 | 0.000 | 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) | 0.000 | 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".