Association between irrigation thresholds and promotion of soil organic carbon decomposition in sandy soil
Bibliographic record
Abstract
Soil organic carbon (SOC) has a significant effect on the carbon cycle, playing a vital role in environmental services and crop production. Increasing SOC stock is identified as an effective way to improve carbon dioxide sequestration, soil health, and plant productivity. Knowing soil water is one of the primary SOC decomposition driver, periods in the crops growth stages with increased water movement might influence the SOC dynamics. Here, we evaluate the temporal effect of four precision irrigation thresholds ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] kPa) in potato crop on SOC dynamics using the Partial Least Square algorithm and the Tea Bag Index in a sandy soil under potato production. The difference of SOC decomposition rate between the precision irrigation thresholds is developed in the second quarter of the growing season, between 38 and 53 days after planting. This critical period occurred in a stage of strong vegetative growth and rapid irrigation cycles. The precision irrigation threshold affected the decomposition rate of SOC. A faster decomposition of labile organic carbon was promoted by water excess ([Formula: see text] kPa). The dryer ([Formula: see text], [Formula: see text], and [Formula: see text] kPa) precision irrigation thresholds did not show any differences. The advancement of this knowledge may promote soil health conservation and carbon sequestration in agricultural soil.
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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 | 0.000 | 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) | 0.001 | 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".