Carbon allocation and fate in paddy soil depending on phosphorus fertilization and water management: results of <sup>13</sup>C continuous labelling of rice
Bibliographic record
Abstract
We grew rice in phosphorus (P) deficient subtropical paddy soil in a field study and used 13 CO 2 continuous labelling to investigate photosynthetic carbon (C) partitioning and allocation under FLOOD versus WET/DRY conditions, with and without P fertilization (80 mg P kg −1 ). The plants and soil were sampled after each of three WET/DRY cycles to determine 13 C allocation in above- and belowground plant biomass, microbial biomass, the rhizosphere, and bulk soil. Irrespective of water management, P-fertilized plants had higher biomass and P content and more total 13 C in the rice-soil system, especially the 13 C incorporation into the shoots (51%–96%), than samples without P fertilization. Root and bulk-soil 13 C were largely independent of both P fertilization and water management. However, by the third sampling, P fertilization had increased the amount of 13 C and microbial biomass 13 C in the rhizosphere soil (RS) by 28% (WET/DRY) and 95% (FLOOD), and by 47% (WET/DRY) and 50% (FLOOD), respectively. The WET/DRY condition had significantly higher microbial biomass and 13 C contents than FLOOD condition only in the RS. These results indicate that a well-established aboveground plant biomass following P fertilization is required to increase belowground C allocation. Thus, WET/DRY conditions, like FLOOD conditions, can provide moisture sufficient for unhindered P availability in rice-paddy system.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".