Experimental and numerical simulation of water movement in soil
Bibliographic record
Abstract
Preferential flow refers to a flow transport pattern where water and solute flows around the soil matrix, contributing to accelerating velocity of movement in the soil; soil recharge occurs over the whole inlet boundary. Preferential flow is produced by a large number of macropores and fractures in the soil, such as root or worm holes, and is very important to groundwater recharge owing to its rapid movement. Using the reaction of iodine turning blue on contact with starch, outdoor tracer experiments of heterogeneous flow in clay loam for different scales and boundary conditions are designed, so that the heterogeneous flow patterns can be studied. Using experimental image analysis, the soil is divided into a matrix and a preferential channel network, formed by the spatial structure of the flow channels. An irregular flow network is established through random generation of angles, lengths, aperture and location coordinates of flow channels, in order to achieve an accurate description of preferential flow channels. According to the soil moisture of each layer obtained by tracer experiments, soil water retention curve and saturated hydraulic conductivity, soil moisture variation was simulated. The distribution of the channel network and models of water movement in the soil are used to undertake a quantitative analysis of the influence of preferential flow on soil water migration.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".