Update docs/src/inverse_problems/structural_identifiability.md

Co-authored-by: Sam Isaacson <[email protected]>

Author: TorkelE

docs/src/inverse_problems/structural_identifiability.md

@@ -7,7 +7,7 @@ Structural identifiability (which is what this tutorial considers) can be illust
 ${dx \over dt} = p1*p2*x(t)$
 where, however much data is collected on *x*, it is impossible to determine the distinct values of *p1* and *p2*. Hence, these parameters are these are non-identifiable.
 
-Catalyst contains a special extension for carrying out structural identifiability analysis using the [StructuralIdentifiability.jl](https://github.com/SciML/StructuralIdentifiability.jl) package. This enables StructuralIdentifiability's `assess_identifiability`, `assess_local_identifiability`, and `find_identifiable_functions` functions to be called directly on Catalyst `ReactionSystem`s. It also implements specialised routines to make these more efficient when applied to reaction network models. How to use these functions are described in the following tutorial, with [StructuralIdentifiability providing a more extensive documentation](https://docs.sciml.ai/StructuralIdentifiability/stable/). If you use this in your research, please [cite the StructuralIdentifiability.jl](@ref structural_identifiability_citation) and [Catalyst.jl](@ref catalyst_citation) publications.
+Catalyst contains a special extension for carrying out structural identifiability analysis using the [StructuralIdentifiability.jl](https://github.com/SciML/StructuralIdentifiability.jl) package. This enables StructuralIdentifiability's `assess_identifiability`, `assess_local_identifiability`, and `find_identifiable_functions` functions to be called directly on Catalyst `ReactionSystem`s. It also implements specialised routines to make these more efficient when applied to reaction network models. How to use these functions is described in the following tutorial, with [StructuralIdentifiability providing a more extensive documentation](https://docs.sciml.ai/StructuralIdentifiability/stable/). If you use this in your research, please [cite the StructuralIdentifiability.jl](@ref structural_identifiability_citation) and [Catalyst.jl](@ref catalyst_citation) publications.
 
 Structural identifiability can be divided into *local* and *global* identifiability. If a model quantity is locally identifiable, it means that its true value can be determined down to a finite-number of possible options. This also means that there is some limited region around its true value, where the true value is the only possible value. Globally identifiable quantities' values, on the other hand, can be uniquely determined. Again, while identifiability can be confirmed structurally for a model, it does not necessarily mean that they are practically identifiable for some given data.