Precipitation frequency controls interannual variation of soil respiration by affecting soil moisture in a subtropical forest plantation
Bibliographic record
Abstract
Despite the significance of interannual variation of soil respiration (R S ) for understanding long-term soil carbon dynamics, factors that control the interannual variation of R S have not been sufficiently investigated. Interannual variation of R S was studied using a 6-year data set collected in a subtropical plantation dominated by an exotic species, slash pine (Pinus elliottii Engelm.), in China. The results showed that seasonal variation of R S was significantly affected by soil temperature and soil water content (SWC). R S in the dry season (July–October) was constrained by seasonal drought. Mean annual R S was estimated to be 736 ± 30 g C·m –2 ·year –1 , with a range of 706–790 g C·m –2 ·year –1 . Although this forest was characterized by a humid climate with high precipitation (1469 mm·year –1 ), the interannual variation of R S was attributed to the changes of annual mean SWC (R 2 = 0.66, P = 0.03), which was affected by annual rainfall frequency (R 2 = 0.80, P < 0.01) and not rainfall amount (P = 0.84). Consequently, precipitation pattern indirectly controlled the interannual variation of R S by affecting soil moisture in this subtropical forest. In the context of climate change, interannual variation of R S in subtropical ecosystems is expected to increase because of the predicted changes of precipitation regime.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Forest ecology study of precipitation frequency and interannual soil respiration; the object is an ecosystem process.
This ecological study examines soil respiration and precipitation in a forest plantation, not research itself.
Forest ecology study of soil respiration and precipitation; environmental domain.
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".