{
  "context": "This criteria evaluates how well the engineer uses the chardet package's SBCS n-gram analysis techniques, specifically focusing on byte normalization, n-gram extraction, binary search pattern matching, and confidence scoring mechanisms that are core to single-byte character set language detection.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Byte Normalization",
      "description": "Implements byte-level text normalization by converting uppercase bytes to lowercase and control characters to spaces, similar to chardet's ByteMap normalization approach used in SBCS recognizers",
      "max_score": 25
    },
    {
      "name": "N-gram Extraction",
      "description": "Extracts 3-byte sequences (trigrams) from normalized text and represents them as 24-bit integer values using bit masking (0xFFFFFF), matching chardet's NGramParser implementation",
      "max_score": 25
    },
    {
      "name": "Binary Search",
      "description": "Uses binary search algorithm to lookup n-grams in a sorted 64-entry pattern list, achieving O(log n) time complexity as implemented in chardet's unrolled binary search for SBCS detection",
      "max_score": 30
    },
    {
      "name": "Confidence Scoring",
      "description": "Calculates confidence score based on the ratio of matched n-grams to total n-grams, applying appropriate scaling (e.g., hitCount/ngramCount × 300) and capping at 100, following chardet's SBCS confidence logic",
      "max_score": 20
    }
  ]
}