Adjust prototype D to include mapping feature from Prototype B

docs/reference/classes-prototype-d.md

@@ -25,13 +25,17 @@ tables in the diagrams below.
 
 ```mermaid
 classDiagram
-  direction LR
 
   Term "1" --> "1..*" Detection
-  Detector "1" --> "0..*" Detection
   Term "1" --> "0..*" Categorization
+  Detection "0..*" --> "1" Detector
+
+  DetectionCategory "0..*" --> "1" Category
+
   Categorization "0..*" --> "1" Category
 
+  Detector "1" --> "0..*" DetectionCategory
+
   class Term
     Term: +Integer id
     Term: +String phrase
@@ -68,10 +72,20 @@ classDiagram
     Categorization: +Integer term_id
     Categorization: +Float confidence
 
+  class DetectionCategory
+    DetectionCategory: +Integer id
+    DetectionCategory: +Integer detector_id
+    DetectionCategory: +Integer category_id
+    DetectionCategory: +Float confidence
+    DetectionCategory: incrementConfidence()
+    DetectionCategory: decrementConfidence()
+
+
   style Term fill:#000,stroke:#66c2a5,color:#66c2a5
 
   style Category fill:#000,stroke:#fc8d62,color:#fc8d62
   style Categorization fill:#000,stroke:#8da0cb,color:#8da0cb
+  style DetectionCategory fill:#000,stroke:#fc8d62,color:#fc8d62
 
   style Detector fill:#000,stroke:#fc8d62,color:#fc8d62
   style Detection fill:#000,stroke:#8da0cb,color:#8da0cb
@@ -92,9 +106,12 @@ Unsolved in this model: one Detector (so far) has `Categories` built into the `D
 
 ### Calculating the category scores
 
-One interesting feature of Detector is that it stores the confidence of each algorithm to accurately predict a category. During validation, if a Detection made by an algorithm is confirmed, we can run `incrementConfidence()` whatever that ends up meaning. Similarly, if an Detection is validated as inaccurate, we can run `decrementConfidence()`.
+One interesting feature of DetectionCategory is that it stores the confidence of each algorithm to accurately predict a category. During validation, if a Detection made by an algorithm is confirmed, we can run `incrementConfidence()` whatever that ends up meaning. Similarly, if an Detection is validated as inaccurate, we can run `decrementConfidence()`.
+
+> [!NOTE]
+>DetectionCategory is a join table represented in Prototype B as `mapping`. This tries to nudge it towards a better name.
 
-Note: Prototype B likely has this feature as well, and has a mapping table between Detector and Category which makes a lot of sense. The confidence is likely actually part of the join table and not part of the Detector itself. Some detectors are themselves non-binary in terms of predection so it is possible that there should be confidence stored for each (namely JournalName detection is fairly weak compared to many other algorithms to date)
+Some detectors are themselves non-binary in terms of prediction so they maintain a confidence level as well (namely JournalName detection is fairly weak compared to many other algorithms to date)
 
 ## Validations