Nick updates to paper

docs/paper.bib

@@ -180,13 +180,13 @@ @misc{Taggart:2022d
     year 		 = {2022},   
     note 		 = {Accessed on September 9, 2023} 
 }
-@misc{loveday2023userfocused,
-      title={A User-Focused Approach to Evaluating Probabilistic and Categorical Forecasts}, 
-      author={Nicholas Loveday and Robert Taggart and Mohammadreza Khanarmuei},
-      year={2023},
-      eprint={2311.18258},
-      archivePrefix={arXiv},
-      primaryClass={stat.AP}
+@article{loveday2024user,
+  title={A User-Focused Approach to Evaluating Probabilistic and Categorical Forecasts},
+  author={Loveday, Nicholas and Taggart, Robert and Khanarmuei, Mohammadreza},
+  journal={Weather and Forecasting},
+  year={2024},
+  publisher={American Meteorological Society},
+  doi={10.1175/WAF-D-23-0201.1}
 }
 @article{nipen2023verif,
   title={Verif: A weather-prediction verification tool for effective product development},
@@ -227,7 +227,8 @@ @misc{loveday2024jive
       year={2024},
       eprint={2404.18429},
       archivePrefix={arXiv},
-      primaryClass={physics.ao-ph}
+      primaryClass={physics.ao-ph},
+	  doi={10.48550/arXiv.2404.18429}
 }
 @article{Ferro_2013, 
 	title={Fair scores for ensemble forecasts: Fair Scores for Ensemble Forecasts}, 

docs/paper.md

@@ -1,12 +1,13 @@
 ---
-title: 'scores: A Python package for verifying accuracy using xarray'
+title: 'scores: A Python package for evaluating and verifying forecasts using xarray'
 tags:
   - Python
-  - geoscience
   - verification
-  - science
-  - earth system science
   - statistics
+  - modelling
+  - geoscience
+  - earth system science
+
 authors:
   - name: Tennessee Leeuwenburg
     orcid: 0009-0008-2024-1967
@@ -17,18 +18,18 @@ authors:
 affiliations:
  - name: Bureau of Meteorology, Australia
    index: 1
-date: 1 December 2023
+date: 17 May 2024
 bibliography: paper.bib 
 
 ---
 
 # Summary
 
-`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It primarily supports the meteorological, climatological and geoscience communities. In addition to supporting the Earth system science communities, it also has wide potential application in machine learning and other domains.
+`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. In addition to supporting the Earth system science communities, it also has wide potential application in machine learning and other domains such as economics.
 
-`scores` not only includes common scores (e.g. MAE, RMSE), it also includes novel scores not commonly found elsewhere (e.g. FIRM, Flip-Flop Index), complex scores (e.g. threshold weighted CRPS), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools.
+`scores` not only includes common scores (e.g. Mean Aboslute Error), it also includes novel scores not commonly found elsewhere (e.g. FIxed Risk Multicategorical (FIRM) score, Flip-Flop Index), complex scores (e.g. threshold-weighted continuous ranked probability score), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools.
 
-All of the scores and statistical techniques in this package have undergone a thorough scientific review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice.
+All of the scores and statistical techniques in this package have undergone a thorough scientific and software review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice.
 
 `scores` is focused on supporting xarray [@Hoyer:2017] datatypes for earth system data. It also aims to be compatible with pandas and geopandas, and to work with NetCDF4, hdf5, Zarr and GRIB data sources among others. Scores is designed to utilise Dask for scaling and performance.
 
@@ -44,13 +45,13 @@ In order to meet the needs of researchers, `scores`:
 
 - is designed to work with n-dimensional data (e.g., geospatial, vertical and temporal dimensions) for both point-based and gridded data. `scores` can effectively handle the dimensionality, data size and data structures commonly utilised for: 
   - gridded earth system data (e.g. Numerical Weather Prediction models)
-  - tabular, point, lat/lon or site-based data (e.g. forecasts for specific locations).
+  - tabular, point, latitude/longitude or site-based data (e.g. forecasts for specific locations).
 - is designed to handle missing data, masking of data and weighting of results.
-- includes a companion Jupyter Notebook for each score, metric and statistical test to demonstrate its use in practice.
+- includes a companion Jupyter Notebook tutorial for each metric and statistical test that demonstrates its use in practice.
 - includes novel scores not commonly found elsewhere (e.g. FIRM [@Taggart:2022a], Flip-Flop Index [@Griffiths:2019; @griffiths2021circular]).
 - is highly modular and avoids extensive dependencies by providing its own implementations where relevant.
-- is intended to be easy to integrate and utilise in a wide variety of environments. It has been tested and used on workstations, servers and in high performance computing (supercomputing) environments. 
-- utilises Dask for scaling and performance.
+- is intended to be easy to integrate and use in a wide variety of environments. It has been tested and used on workstations, servers and in high performance computing (supercomputing) environments. 
+- uses Dask for scaling and performance.
 
 ## Metrics, Statistical Techniques and Data Processing Tools Included in Scores 
 
@@ -66,19 +67,15 @@ Here is a **curated selection** of the metrics, tools and statistical tests curr
 | **[Probability](https://scores.readthedocs.io/en/latest/included.html#probability)**       	|Scores for evaluating forecasts that are expressed as predictive distributions, ensembles, and probabilities of binary events.                 	|Brier Score, Continuous Ranked Probability Score (CRPS) for Cumulative Distribution Functions (CDFs) (including threshold-weighting, see [@Gneiting:2011]), CRPS for ensembles [@Gneiting_2007; @Ferro_2013], Receiver Operating Characteristic (ROC), Isotonic Regression (reliability diagrams) [@dimitriadis2021stable].              	|
 | **[Categorical](https://scores.readthedocs.io/en/latest/included.html#categorical)**       	|Scores for evaluating forecasts based on categories.                	|Probability of Detection (POD), False Alarm Rate (FAR), Probability of False Detection (POFD), Success Ratio, Accuracy, Peirce's Skill Score, Critical Success Index (CSI), Gilbert Skill Score, Heidke Skill Score, Odds Ratio, Odds Ratio Skill Score, F1 score, FIxed Risk Multicategorical (FIRM) Score [@Taggart:2022a].               	|
 | **[Statistical Tests](https://scores.readthedocs.io/en/latest/included.html#statistical-tests)** 	|Tools to conduct statistical tests and generate confidence intervals.                 	| Diebold-Mariano [@Diebold:1995] with both the [@Harvey:1997] and [@Hering:2011] modifications.              	|
-| **[Processing tools](https://scores.readthedocs.io/en/latest/included.html#processing-tools-for-preparing-data)**        	|Tools to pre-process data.                 	|Data matching, Discretization, Cumulative Density Function Manipulation.              	|
+| **[Processing Tools](https://scores.readthedocs.io/en/latest/included.html#processing-tools-for-preparing-data)**        	|Tools to pre-process data.                 	|Data matching, Discretisation, Cumulative Density Function Manipulation.              	|
 
 Additionally, `scores` has an area specifically to hold emerging scores which are still undergoing research and development. This provides a clear mechanism for people to share, access and collaborate on new scores, and be able to easily re-use versioned implementations of those scores. 
 
 ## Use in Academic Work
 
-In 2015, the Australian Bureau of Meteorology began developing a new verification system called Jive. For a description of Jive see [@loveday2024jive].
-
-The Jive verification metrics have been used to support several publications [@Griffiths:2017; @Foley:2020; @Taggart:2022b; @Taggart:2022c; @Taggart:2022d].
-
-`scores` has arisen from the Jive verification system. `scores` includes mathematical functions from Jive and is intended to modularise these functions and make them available as an open source package. 
+In 2015, the Australian Bureau of Meteorology began developing a new verification system called Jive. For a description of Jive see [@loveday2024jive]. The Jive verification metrics have been used to support several publications [@Griffiths:2017; @Foley:2020; @Taggart:2022b; @Taggart:2022c; @Taggart:2022d]. `scores` has arisen from the Jive verification system and was created to modularise the Jive verification functions and make them available as an open source package. 
 
-`scores` has been used to explore user-focused approaches to evaluating probabilistic and categorical forecasts [@loveday2023userfocused].
+`scores` has been used to explore user-focused approaches to evaluating probabilistic and categorical forecasts [@loveday2024user].
 
 ## Related Software Packages