Resolve minor lint issues

README.md

@@ -19,4 +19,4 @@ EchoSMs documentation is available [here](https://ices-tools-dev.github.io/echoS
 
 ## Contributing
 
-We welcome all contributions to echoSMs, be it code, test cases, bug reports, discussion of models, etc. Guidance on this is available in the echoSMs [documentation](https://ices-tools-dev.github.io/echoSMs/contributing/).
+We welcome all contributions to echoSMs, be it code, test cases, bug reports, discussion of models, etc. Guidance on this is available in the echoSMs [documentation](https://ices-tools-dev.github.io/echoSMs/contributing/).

docs/benchmark_data.md

@@ -50,9 +50,3 @@ The column names and descriptions are:
 | Cylinder_PressureRelease | Benchmark values for the pressure release cylinder. TS values for end-on (0°) incidence were not computed. |
 | Cylinder_Gas | Benchmark values for the gas filled cylinder. TS values for end-on (0°) incidence were not computed. |
 | Cylinder_WeaklyScattering | Benchmark values for the weakly scattering cylinder. TS values for end-on (0°) incidence were not computed. |
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docs/index.md

@@ -4,7 +4,7 @@
 
 ## Background
 
-This project is an international collaboration that is, in part, a component of a U.S. NOAA-Fisheries active acoustic strategic initiative, [AA-SI](https://github.com/nmfs-fish-tools/AA-SI/tree/main). 
+This project is an international collaboration that is, in part, a component of a U.S. NOAA-Fisheries active acoustic strategic initiative, [AA-SI](https://github.com/nmfs-fish-tools/AA-SI/tree/main).
 
 Quantitative interpretation of acoustic echograms requires software expertise to develop advanced analytical methods for echo classification using mathematical models that predict acoustic backscatter (e.g., target strength, TS re 1 m² [dB]). These models and predictions can be used to inform echo classification by validating empirical measurements and generating training data for machine learning (ML), artificial intelligence (AI), and other advanced analytical methods, such as inverse methods. Application of these models to fish and plankton requires anatomical and morphological data that are easily accessible and available to the models.
 
@@ -97,4 +97,4 @@ The approximate analytical models,shapes, and supported boundary conditions will
 12. [Jones et al. 2009.](https://doi.org/10.1121/1.3021298) Use of the distorted wave Born approximation to predict scattering by inhomogeneous objects: Application to squid. JASA. 125: 73-88.
 13. [Demer and Conti. 2003.](https://doi.org/10.1016/S1054–3139(03)00002-X) Reconciling theoretical versus empirical target strengths of krill: Effects of phase variability on the distorted wave Born approximation. ICES J. Mar. Sci. 60: 429-434.
 14. [Demer and Conti. 2004.](https://doi.org/10.1016/j.icesjms.2003.12.003) Erratum: Reconciling theoretical versus empirical target strengths of krill; effects of phase variability on the distorted-wave, Born approximation. ICES J. Mar. Sci. 61: 157-158.
-15. _TBC_
+15. _TBC_