Update docs/src/inverse_problems/structural_identifiability.md

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

Author: TorkelE

docs/src/inverse_problems/structural_identifiability.md

@@ -87,7 +87,7 @@ nothing # hide
 giving a minimum bound of $99.9\%$ chance of correctness. In practise, the bounds used by StructuralIdentifiability are very conservative, which means that while the minimum guaranteed probability of correctness in the default case is $99\%$, in practise it is much higher. While increasing the value of `p` increases the certainty of correctness, it will also increase the time required to assess identifiability.
 
 ## Local identifiability analysis
-Local identifiability can be assessed through the `assess_local_identifiability` function. While this is already determined by `assess_identifiability`, assessing local identifiability only have the advantage that it is easier to compute. Hence, there might be models where global identifiability analysis fails (or takes prohibitively long time), where instead `assess_local_identifiability` can be used. This functions takes the same inputs as `assess_identifiability` and returns, for each quantity, `true` if it is locally identifiable (or `false` if it is not). Here, for the Goodwind oscillator, we assesses it for local identifiability only:
+Local identifiability can be assessed through the `assess_local_identifiability` function. While this is already determined by `assess_identifiability`, assessing local identifiability only has the advantage that it is easier to compute. Hence, there might be models where global identifiability analysis fails (or takes a prohibitively long time), where instead `assess_local_identifiability` can be used. This function takes the same inputs as `assess_identifiability` and returns, for each quantity, `true` if it is locally identifiable (or `false` if it is not). Here, for the Goodwind oscillator, we assesses it for local identifiability only:
 ```example si1
 assess_local_identifiability(goodwind_oscillator; measured_quantities=[:M], loglevel=Logging.Error)
 ```