Dynamic Coupling of SWAT+ with System Dynamics Models using Tinamït and a Socket Based Protocol
Bibliographic record
Abstract
<p>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.</p><p>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.</p>
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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 | 0.000 |
| 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".