Edition before publication

article/Makefile

@@ -6,7 +6,7 @@ images:
 	montage processing_speed_1-1.png processing_speed_1-2.png -tile 2x -gravity south -geometry +1+1 -bordercolor white -border 5x5 processing-speed.png
 
 build:
-	pandoc --standalone --filter $(HOME)/.cabal/bin/pandoc-crossref --template=rescience-template.tex --latex-engine=xelatex --biblatex --bibliography=article.bib -M "crossrefYaml=crossref.yaml" --output article.tex article.md
+	pandoc --standalone --filter ${HOME}/.cabal/bin/pandoc-crossref --template=rescience-template.tex --latex-engine=xelatex --biblatex --bibliography=article.bib -M crossrefYaml=crossref.yaml --output article.tex article.md
 	xelatex article
 	biber article
 	xelatex article

article/article.md

@@ -9,20 +9,20 @@ Address:
 Contact:
   - [email protected]
 Editor:
-  - Name Surname
+  - Karthik Ram
 Reviewer:
-  - Name Surname
-  - Name Surname
+  - Timothée Poisot
+  - Dan McGlinn
 Publication:
-  received:  Sep,  1, 2015
-  accepted:  Sep, 1, 2015
-  published: Sep, 1, 2015
-  volume:    "**1**"
+  received:  Nov, 3, 2015
+  accepted:  March, 2, 2016
+  published: March, 7, 2016
+  volume:    "**2**"
   issue:     "**1**"
-  date:      Nov 2015
+  date:      Feb 2016
 Repository:
-  article:   "http://github.com/jsta/rescience-submission/tree/STACHELEK"
-  code:      "http://github.com/rescience/rescience-submission/tree/STACHELEK/code"
+  article:   "https://github.com/ReScience-Archives/Stachelek-2016/tree/master/article"
+  code:      "https://github.com/ReScience-Archives/Stachelek-2016/tree/master/code"
   data:      
   notebook:  
 Reproduction:
@@ -44,21 +44,21 @@ The following reference implementation was based on the model description and so
 
 # Results
 
-![Node-edge selection ensures that all relevant landscape features are retained in the accumulated cost surface. Note that the triangulation in the second panel incudes Null data nodes. These are trimmed prior to construction of the final graph.](node-edge_selection.png) {#fig:nodeselection}
+![Node-edge selection ensures that all relevant landscape features are retained in the accumulated cost surface. Note that the triangulation in the second panel incudes Null data nodes. These are trimmed prior to construction of the final graph.](node-edge_selection.png){#fig:nodeselection}
 
 First, I reproduced the basic output of Figures 3 and 4 using model inputs obtained from the author of the original article (Figures @fig:nodeselection , @fig:irlconstruction). I tested a range of different algorithms for producing Delaunay triangulations before settling on the same underlying algorithm as the original [@quickhull].
 
-![Accumlated cost surface construction begins by traversing the graph from the starting-node (open circle) to the remaining points in the landscape graph. In the final step, missing nodes are imputed according to a nearest neighbor selection.](irl-construction.png) {#fig:irlconstruction}
+![Accumlated cost surface construction begins by traversing the graph from the starting-node (open circle) to the remaining points in the landscape graph. In the final step, missing nodes are imputed according to a nearest neighbor selection.](irl-construction.png){#fig:irlconstruction}
 
 Next, I reproduced the performance comparisons in Figure 7 (Figure @fig:performance). The results suggest a more nuanced interpretation of the relative performance of the two methods. Although initial construction was much faster for regular landscape graphs, at a sufficiently high number of starting nodes the initial performance penalty afforded to irregular landscape graphs was outweighed by a decrease in per starting-node processing time. These findings can be attributed to the fact that the simple structures of regular landscape graphs are amenable to matrix operations and that irregular landscape graphs have a lower number of node/edge features.
 
-![Performance comparisons between regular and irregular landscape graphs. Note that for only two source cells, the performance benefit realized by the irregular landscape graph was outweighed by a higher initialization cost.](processing-speed.png) {#fig:performance}
+![Performance comparisons between regular and irregular landscape graphs. Note that for only two source cells, the performance benefit realized by the irregular landscape graph was outweighed by a higher initialization cost.](processing-speed.png){#fig:performance}
 
 Profiling of the reference implementation code revealed that the bulk of the processing time required to construct irregular landscape graphs was spent on Delaunay triangulation. Note that the proposed reference implementation uses compiled Fortran code [@quickhull] to implement Delaunay triangulations and compiled C code from the igraph package [@igraph] to construct graphs and calculate accumulated cost distances. 
 
 Finally, I reproduced the directional bias tests in Figure 8 (Figure @fig:directionalbias). As in the original article, I found that regular graphs produced directionally-biased cost surfaces. However, I was able to correct for these biases by scaling graph edge weights according to the diagonal distance between grid cells (see the `gdistance::geoCorrection` function [@gdistance]).
 
-![Comparison of directional bias between irregular, regular, and corrected-regular landscape graphs.](directional_bias-1.png) {#fig:directionalbias}
+![Comparison of directional bias between irregular, regular, and corrected-regular landscape graphs.](directional_bias-1.png){#fig:directionalbias}
 
 # Conclusion