Water-filled microbially habitable pores: Relation to denitrification
Bibliographic record
Abstract
The onset of a decline in net N mineralization, primarily due to denitrification, has been related to water contents, θW, or water-filled porosities, fW, but values of these characteristics vary among soils thus limiting their use as diagnostic criteria. An implicit assumption in the use of these characteristics is that water-filled pores of all sizes are habitable by microorganisms participating in denitrification. However, microorganisms are excluded from small pores, and the volume fraction of these pores varies with soil structure. The objective of this study was to determine if variation in the volume fraction of water-filled microbially habitable pores, θMHP, contributes to the variation in denitrification in soils of different structure. Data were used from studies with and without growing maize (Zea maize L.) plants. Variation in soil structure was achieved by using soils of different texture and organic carbon contents that were packed to two different levels of relative compaction. At the onset of a decrease in net N mineralization, values of θMHP exhibited less variability among soils than either θW or fW. The θMHP will be of greatest value as a diagnostic criterion for the decline in net N mineralization in soils exhibiting variation in the volume fraction of pores ≤ 4 µm diameter. Key words: Denitrification, habitable pore space, soil structure
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".