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+# Prototype C ("Detections with confidence")
+
+This prototype relies on fewer tables, with one record in each, and leans more heavily on behavior in code.
+
+> [!WARN]
+> The intent was to collapse Categorizations into Detections by moving booleans to floats, but this looses important
+nuance from the original prototype A-minus it was based on.
+
+## Shared preface
+
+The same color scheme is used for both prototypes:
+
+* <font style="color:#66c2a5">Terms</font>, which flow in continuously with Search Events;
+* A <font style="color:#fc8d62">knowledge graph</font>, which includes the categories, detectors, and relationships
+  between the two which TACOS defines and maintains, and which is consulted during categorization; and
+* The <font style="color:#8da0cb">linkages between these terms and the graph</font>, which record which signals are
+  detected in each term, and how those signals are interpreted to place the term into a category.
+
+A simple way to describe the Categorization workflow would be to say that Categorization involves populating the blue
+tables in the diagrams below.
+
+## Categorization
+
+```mermaid
+classDiagram
+  direction LR
+
+  Term --< Detection: has many
+
+  class Term
+    Term: +Integer id
+    Term: +String phrase
+    Term: calculateCategory()
+
+
+  class Detection
+    Detection: +Integer id
+    Detection: +Integer term_id
+    Detection: +Integer detector_version
+    Detection: +Float DOI
+    Detection: +Float ISBN
+    Detection: +Float ISSN
+    Detection: +Float PMID
+    Detection: +Float Journal
+    Detection: +Float SuggestedResource
+    Detection: initialize()
+    Detection: setDetectionVersion()
+    Detection: recordDetections()
+    Detection: recordPatterns()
+    Detection: recordJournals()
+    Detection: recordSuggestedResource()
+
+  style Term fill:#000,stroke:#66c2a5,color:#66c2a5
+
+  style Category fill:#000,stroke:#fc8d62,color:#fc8d62
+  style Detector fill:#000,stroke:#fc8d62,color:#fc8d62
+
+  style Detection fill:#000,stroke:#8da0cb,color:#8da0cb
+```
+
+### Order of operations
+
+1. A new `Term` is registered.
+2. A `Detection` record for that `Term` is created (which allows repeat detection operations as TACOS gains new
+   capabilities). Rather than storing a boolean, we store a float to represent how confident we are that the detection is able to be used for categorization. This approach feels flawed
+
+### Category values
+
+Not worked out as the model seems flawed and was abandoned after initial discussion.
+
+### Calculating the category scores
+
+Not worked out as the model seems flawed.
+
+## Validations
+
+Not worked out as the model seems flawed.