Modeling water fluxes through containerized soilless substrates using HYDRUS
Bibliographic record
Abstract
Abstract Containerized crop production can enhance plant health and ensure environmental sustainability, yet proper management requires improved understanding of water fluxes and storage within soilless substrates. Numerical simulation tools developed to simulate water movement in porous media, such as HYDRUS‐3D, may help to quantify effective hydraulic properties of soilless substrates but have not yet been tested in this capacity. Therefore, this study had three main objectives: (a) to assess the accuracy of HYDRUS‐3D for simulating water flow through peat‐ and bark‐based soilless substrates by comparing measured and modeled drainage and water storage; (b) to determine sensitivity of model outputs to individual hydraulic parameters; and (c) to compare model parameterization using three laboratory characterization methods (instantaneous profile sorption, instantaneous profile desorption, and evaporation) vs. inverse modeling with HYDRUS. The results showed that the modeled water contents and drainage timing and amounts were most sensitive to saturated volumetric water content (θ s ) and least sensitive to saturated hydraulic conductivity ( K s ). With regard to parameterization methods, the inverse modeling approach provided the most accurate water balance simulations for both substrates, followed by the sorption method. These two methods estimated lower peak water contents and greater drainage compared with simulations parameterized using desorption and evaporation measurements. Overall, the study results showed that the Richards equation, as calculated using HYDRUS, can provide accurate simulations of water flux through containers when properly calibrated, though sorption‐derived parameters may suffice when model optimization is impractical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".