Effect of long-term irrigation and tillage practices on X-ray CT and gas transport derived pore-network characteristics
Bibliographic record
Abstract
The gas transport parameters, diffusivity and air-filled porosity are crucial for soil aeration, microbial activity and greenhouse gas emission, and directly depend on soil structure. In this study, we analysed the effect of long-term tillage and irrigation practices on the surface structure of an arable soil in New Zealand. Our hypothesis was that topsoil structure would change under intensification of arable production, affecting gas exchange. Intact soil cores were collected from plots under intensive tillage (IT) and direct drill (DD), irrigated or rainfed. In total, 32 cores were scanned by X-ray computed tomography (CT) to derive the pore network >30 µm. The cores were then used to measure soil-gas diffusivity, air-permeability and air-filled porosity of pores close to the resolution of the X-ray CT scans, namely =30 µm. The gas measurements allow the calculation of pore-network connectivity and tortuosity parameters, which were compared with the CT-derived structural characteristics. Long-term irrigation had little effect on any of the parameters analysed. Total porosity tended to be lower under IT than DD, whereas the CT-derived porosity was comparable. Both the CT-derived mean pore diameter (MPD) and other morphological parameters, as well as gas measurement-derived parameters, highlighted a less developed structure under IT. The differences in the functional pore-network structure were attributed to SOC depletion and the mechanical disturbance through IT. Significant correlations between CT-derived parameters and functional gas transport parameters such as tortuosity and MPD were found, which suggest that X-ray CT could be useful in the prediction of gas transport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".