Mechanistic Simulation of Salt‐Affected Soil‐Plant‐Atmosphere Continuum Dynamics in Seasonally Frozen Regions
Bibliographic record
Abstract
Abstract The salt‐affected Soil‐Plant‐Atmosphere Continuum (SPAC) is a dynamic, interactive system that is particularly complex in seasonally frozen regions where salt transport, precipitation‐dissolution, and soil freeze‐thaw processes play crucial, interrelated roles. Understanding these coupled processes and representing them with mathematical models is critical for effective management of SPAC systems. This study presents a new mechanistic approach and an improved model that integrates a chemical equilibrium module within a mechanistic‐based transport computational module (modified SHAW model). The chemical equilibrium module determines salt precipitation‐dissolution using thermodynamic theory and explains the effects of efflorescence and subflorescence on system dynamics. The model enables simultaneous solutions for heat, water, and salt transport with chemical equilibrium throughout non‐freezing and freezing seasons, as well as plant growth dynamics. Assessment of the model using laboratory experiments and field studies showed good performance, with coefficient of determination values exceeding 0.65 for simulated and measured evaporation rate, leaf area index, soil water content, salt content, and temperature. Furthermore, a comparison between simulation results considering and neglecting the impact of salt precipitation‐dissolution highlights potential inaccuracies in soil heat‐water‐salt dynamics and plant water use resulting from the omission of this process in mechanistic models.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".