Dynamic Coupling of SWAT+ with System Dynamics Models using Tinamït and a Socket Based Protocol
Bibliographic record
Abstract
Effective and sustainable decision making in water resources management often requires modelling techniques that are able to account for the inherent complexity of coupled human-water systems. One approach that is used to model coupled human-water systems is to couple physically based models and system dynamics models. However, in many cases, this type of model coupling is labour-intensive and time-consuming, which can hinder its routine use in modeling and decision making. Tinamït, a Python package, is an application programming interface (API) that provides definitions of functions and objects that simplify, in this case, coupled model building. Tinamït supports automatic SD model wrapping and coupling to specialized physically-based models, which makes it particularly useful for coupled human-water systems modelling. In this research, a connection between SWAT+ (a FORTRAN-based hydrological model) and the Tinamït API is established through model wrapping. This wrapping approach takes advantage of both the agility of sockets and the wide applicability of JavaScript Object Notation (JSON) to transmit data between FORTRAN and Python routines at runtime. Any model that runs in SWAT+ can now be automatically coupled to SD models through the Tinamït API, without the need for extensive programming, therefore facilitating wider application of coupled modelling techniques for integrated policy development and decision making in the field of coupled human-water systems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".