add misc missing references

Author: TorkelE

docs/src/model_creation/dsl_advanced.md

@@ -28,7 +28,7 @@ end
 parameters(catalysis_sys)
 ```
 !!! note
-    When declaring species using the `@species` option, the species symbol must be followed by `(t)`. The reason is that species are time-dependent variables, and this time-dependency must be explicitly specified ([designation of non-`t` dependant species is also possible](@ref ref)).
+    When declaring species using the `@species` option, the species symbol must be followed by `(t)`. The reason is that species are time-dependent variables, and this time-dependency must be explicitly specified ([designation of non-`t` dependant species is also possible](@ref dsl_advanced_options_ivs)).
 
 Similarly, the `@parameters` option can be used to explicitly designate something as a parameter:
 ```@example dsl_advanced_explicit_definitions

docs/src/model_creation/dsl_basics.md

@@ -267,7 +267,7 @@ Catalyst comes with the following predefined functions:
 - The activating/repressive Hill function: $hillar(X,Y,v,K,n) = v * (X^n)/(X^n + Y^n + K^n)$.
 
 ### [Time-dependant rates](@id dsl_description_nonconstant_rates_time)
-Previously we have assumed that the rates are independent of the [time variable, $t$](@ref ref). However, time-dependent reactions are also possible. Here, simply use `t` to represent the time variable. E.g., to create a production/degradation model where the production rate decays as time progresses, we can use:
+Previously we have assumed that the rates are independent of the time variable, $t$. However, time-dependent reactions are also possible. Here, simply use `t` to represent the time variable. E.g., to create a production/degradation model where the production rate decays as time progresses, we can use:
 ```@example dsl_basics
 rn_14 = @reaction_network begin
     kp/(1 + t), 0 --> P

docs/src/model_creation/examples/programmatic_generative_linear_pathway.md

@@ -1,5 +1,5 @@
 # [Programmatic, generative, modelling of a linear pathway](@id programmatic_generative_linear_pathway)
-This example will show how to use programmatic, generative, modelling to model a system implicitly. I.e. rather than listing all system reactions explicitly, the reactions are implicitly generated from a simple set of rules. This example is specifically designed to show how [programmatic modelling](@ref ref) enables *generative workflows* (demonstrating one of its advantages as compared to [DSL-based modelling](@ref dsl_description)). In our example, we will model linear pathways, so we will first introduce these. Next, we will model them first using the DSL, and then using a generative programmatic workflow.
+This example will show how to use programmatic, generative, modelling to model a system implicitly. I.e. rather than listing all system reactions explicitly, the reactions are implicitly generated from a simple set of rules. This example is specifically designed to show how [programmatic modelling](@ref programmatic_CRN_construction) enables *generative workflows* (demonstrating one of its advantages as compared to [DSL-based modelling](@ref dsl_description)). In our example, we will model linear pathways, so we will first introduce these. Next, we will model them first using the DSL, and then using a generative programmatic workflow.
 
 ## [Linear pathways](@id programmatic_generative_linear_pathway_intro)
 Linear pathways consists of a series of species ($X_0$, $X_1$, $X_2$, ..., $X_n$) where each activates the subsequent one. These are often modelled through the following reaction system:
@@ -75,7 +75,7 @@ plot!(sol_n10; idxs = :X10, label = "n = 10")
 ```
 
 ## [Modelling linear pathways using programmatic, generative, modelling](@id programmatic_generative_linear_pathway_generative)
-Above, we investigated the impact of linear pathways' lengths on their behaviours. Since the models were implemented using the DSL, we had to implement a new model for each pathway (in each case writing out all reactions). Here, we will instead show how [programmatic modelling](@ref ref) can be used to generate pathways of arbitrary lengths.
+Above, we investigated the impact of linear pathways' lengths on their behaviours. Since the models were implemented using the DSL, we had to implement a new model for each pathway (in each case writing out all reactions). Here, we will instead show how [programmatic modelling](@ref programmatic_CRN_construction) can be used to generate pathways of arbitrary lengths.
 
 First, we create a function, `generate_lp`, which creates a linear pathway model of length `n`. It utilises [*vector variables*](@ref ref) to create an arbitrary number of species, and also creates an [observable](@ref ref) for the final species of the chain.
 ```@example programmatic_generative_linear_pathway_generative

docs/src/model_creation/model_visualisation.md

@@ -64,7 +64,7 @@ savegraph(repressilator_graph, "repressilator_graph.png")
 rm("repressilator_graph.png") # hide
 ```
 
-Finally, a [network's reaction complexes](@ref ref) (and the reactions in between these) can be displayed using the `complexgraph(brusselator)` function:
+Finally, a [network's reaction complexes](@ref network_analysis_reaction_complexes) (and the reactions in between these) can be displayed using the `complexgraph(brusselator)` function:
 ```@example visualisation_graphs
 complexgraph(brusselator)
 ```

docs/src/model_creation/network_analysis.md

@@ -101,7 +101,7 @@ Let's check these are equal symbolically
 isequal(odes, odes2)
 ```
 
-## Reaction complex representation
+## [Reaction complex representation](@id network_analysis_reaction_complexes)
 We now introduce a further decomposition of the RRE ODEs, which has been used to
 facilitate analysis of a variety of reaction network properties. Consider a simple
 reaction system like

docs/src/model_simulation/simulation_introduction.md

@@ -267,7 +267,7 @@ nothing # hide
 ```
 If the `@default_noise_scaling` option is used, that term is only applied to reactions *without* `noise_scaling` metadata.
 
-While the `@default_noise_scaling` option is unavailable for [programmatically created models](@ref ref), the [`remake_reactionsystem`](@ref) function can be used to achieve a similar effect.
+While the `@default_noise_scaling` option is unavailable for [programmatically created models](@ref programmatic_CRN_construction), the [`remake_reactionsystem`](@ref) function can be used to achieve a similar effect.
 
 ## [Performing jump simulations using stochastic chemical kinetics](@id simulation_intro_jumps)
 

docs/src/model_simulation/simulation_structure_interfacing.md

@@ -21,7 +21,7 @@ oprob = ODEProblem(cc_system, u0, tspan, ps)
 nothing    # hide
 ```
 
-We can find a specie's (or [variable's](@ref ref)) initial condition value by simply indexing with the species of interest as input. Here we check the initial condition value of $C$:
+We can find a species's (or [variable's](@ref ref)) initial condition value by simply indexing with the species of interest as input. Here we check the initial condition value of $C$:
 ```@example structure_indexing
 oprob[:C]
 ```
@@ -162,7 +162,7 @@ get_S(oprob)
 ```
 
 ## [Interfacing using symbolic representations](@id simulation_structure_interfacing_symbolic_representation)
-As [previously described](@ref ref), when e.g. [programmatic modelling is used](@ref ref), species and parameters can be represented as *symbolic variables*. These can be used to index a problem, just like symbol-based representations can. Here we create a simple [two-state model](@ref rbasic_CRN_library_two_statesef) programmatically, and use its symbolic variables to check, and update, an initial condition:
+As [previously described](@ref ref), when e.g. [programmatic modelling is used](@ref programmatic_CRN_construction), species and parameters can be represented as *symbolic variables*. These can be used to index a problem, just like symbol-based representations can. Here we create a simple [two-state model](@ref rbasic_CRN_library_two_statesef) programmatically, and use its symbolic variables to check, and update, an initial condition:
 ```@example structure_indexing_symbolic_variables
 using Catalyst
 t = default_t()