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 13CO2 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 13C 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 13C in the rice-soil system, especially the 13C incorporation into the shoots (51%–96%), than samples without P fertilization. Root and bulk-soil 13C were largely independent of both P fertilization and water management. However, by the third sampling, P fertilization had increased the amount of 13C and microbial biomass 13C 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 13C 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 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".