Soil phosphorus improved the soil organic carbon of converted forestland from cropland within southern Qinling-Daba Mountains, China
Bibliographic record
Abstract
Previous studies showed that the program of converted forestland from cropland (CFC), initiated by the Chinese government in 1999, has been a significant contributor to China’s efforts towards carbon neutrality. Here, the 20-year CFCs of two aspects (sunny and shady) and three positions (upper, middle, and lower) hillslopes, adjacent maize ( Zea mays L.) cropland, and natural secondary Castanea mollissima forest (CCF) within southern Qinling-Daba Mountains region (Qinba) had been selected as the targets. The soil bulk density (SBD), soil organic carbon concentration (SOCC), total nitrogen (TN), and total phosphorus (TP) had been determined. The results showed that SBD increased with depth, and other parameters decreased, which varied largely with the aspect and position. The SOC stocks (SOCS) of CCF, cropland, and CFC were 152.81 ± 5.17, 168.19 ± 11.87, and 183.92 ± 35.69 Mg C hm−2, respectively. The SOCCs of CCF, cropland, and CFC were 17.71 ± 4.38, 20.23 ± 5.28, and 21.89 ± 7.33 g kg−1, respectively. The CFC increased the correlations between SOC and TP, and decreased the correlations between SOC and TN. The CFC shifted the relationships of lg SOC versus lg N:P and lg SOC versus lg TP from decreasing returns of cropland to isometric. Overall, the CFCs enhanced SOC, especially in the middle shady hillslopes within the southern middle-mountain of Qinba. In contrast, SOC levels decreased in the sunny upper hillslopes. We presented the evidence that hillslopes aspect and position had significant effects on SOC, which was regulated by soil phosphorus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".