Experimental flooding shifts carbon, nitrogen, and phosphorus pool distribution and microbial activity
Bibliographic record
Abstract
Abstract Flooding transforms the soil environment, impacting small-scale controls on mineral associated carbon (C), nitrogen (N) and phosphorus (P) stabilization and mobilization. Yet during flood events, mineral associated C, N, and P may not respond in the same way, such that soluble C, N, and P concentrations and stoichiometry may change with unknown consequences for microbial activity. Using a laboratory incubation approach, we investigated how flooding impacts C, N, and P pool distribution and microbial activity across a 1-week flood event and after drying. We found that all three mineral associated pools responded dynamically to flooding, increasing and decreasing throughout the flood with a net increase of 5.9% in mineral associated C and 32.5% decrease in residual P. However, mineral associated C, N, and P each shifted at different temporal points, indicating that they are likely responding to separate destabilization mechanisms working at different temporal scales. The soluble C and N responses to flooding contrasted the mineral associated pool’s response, increasing by 57% and decreasing by 72% respectively at the beginning of the flood which remained post-flood. However, soluble P behaved more similarly to the mineral associated pool. The microbial community maintained and even increased their exo-cellular activity throughout the flood period, responding most strongly to changes in available P. Our research demonstrates that the mineral associated pool is sensitive to short-term flooding altering the composition and quantity of water extracted compounds and microbial activity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".