Distinctiveness Uniqueness Heuristic Rater
Distinctness against an explicitly supplied independently nearest reference using aligned categorical attributes; not universal uniqueness or registry clearance.
Description
Distinctiveness against a supplied reference can be described by exact categorical differences.
Distinctiveness against a supplied reference can be described by exact categorical differences.1
This tool uses your independently selected nearest reference and attribute weights rather than searching a flag registry.
When to use Distinctiveness Uniqueness Heuristic Rater
Use this tool when you need to reproduce the stated calculation from measured records or independently selected model inputs. Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.
How to use Distinctiveness Uniqueness Heuristic Rater and understand its results
- Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.
- Enter candidateAttributeIds, callerNearestReferenceAttributeIds, callerAttributeWeights, following the field table and rules below, then run the tool.
- Read result. Use the output meanings below to interpret units, signs, flags, and any nested result fields.
- Correct reported input issues and run again. For a successful result, check the stated evidence coverage and assumptions before using it in your review.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| candidateAttributeIds | Candidate attribute ids | list of string | Required |
| callerNearestReferenceAttributeIds | Caller nearest reference attribute ids | list of string | Required |
| callerAttributeWeights | Caller attribute weights | list of number | Required |
Use aligned nonempty candidate and reference attribute lists and nonnegative weights with positive total. Sum weights of exact mismatches and divide by total; retain every differing zero-based attribute index.
distinctness=Σ(w_i×[candidate_i≠reference_i])/Σw Symbols follow the input and output meanings and the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Calculated observation or reference | object |
| result.weightedExactDistinctness | Weighted Exact Distinctness | number |
| result.differingAttributeIndices | Differing Attribute Indices | list |
{
"input": {
"candidateAttributeIds": [
"red",
"cross"
],
"callerNearestReferenceAttributeIds": [
"blue",
"cross"
],
"callerAttributeWeights": [
1,
3
]
},
"output": {
"result": {
"weightedExactDistinctness": 0.25,
"differingAttributeIndices": [
0
]
}
}
}Limitations and assumptions
The calculation uses supplied geometry, observations, or annotations. It does not establish an official construction specification, national identity, or universal design quality.
Missing required fields, unknown fields, wrong types, and violated calculation rules are input issues. Numeric values must be finite; nonfinite results are rejected. Evidence arrays must be dense, with no missing cells. Numeric observation lists allow at most 10,000 entries, subject to any smaller bounds stated above. String lists allow at most 128 strings of at most 128 characters. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related review, use Lookalike Confusion Risk Scorer: Lookalike confusion is an observed response of a particular observer cohort to a flag pair. This tool reads your matched calibration at an in-range coordinate and retains the local response slope. For a complementary step, use Flag Difference Hamming Pixel Comparator: Hamming distance counts exact mismatches between categorical cells. For registered flag grids it measures label differences without introducing resampling or perceptual color judgments.
References
Similar or alternative tools
- Lookalike Confusion Risk Scorer
Lookalike confusion is an observed response of a particular observer cohort to a flag pair. This tool reads your matched calibration at an in-range coordinate and retains the local response slope.
- Flag Difference Hamming Pixel Comparator
Hamming distance counts exact mismatches between categorical cells. For registered flag grids it measures label differences without introducing resampling or perceptual color judgments.