Significance Value Score Weighted Sum Calculator
Restricted institution-calibrated three-domain significance summary, not universal value, legal status or an automatic treatment priority. This restricted caller-defined rubric has three independently declared criterion domains: independently assessed historical significance, independently assessed artistic significance, independently assessed research/community significance. Scores 0–100, nonnegative calibrated weights; known-only normalized mean and unresolved weight retained. No universal validated model, population risk prediction, personal diagnosis or authorization.
Description
Collection significance can be summarized through an independently adopted criterion rubric.
Collection significance can be summarized through an independently adopted criterion rubric.1
Weighting known evidence does not supply missing assessments or validate that rubric.
When to use Significance Value Score Weighted Sum Calculator
Use this tool to calculate or review the supplied observations under the stated rules. Establish the object or visitor scope, measurement units and independently applicable conservation, engineering, access or editorial reference.
How to use Significance Value Score Weighted Sum Calculator and understand its results
- Establish the object or visitor scope, measurement units and independently applicable conservation, engineering, access or editorial reference.
- Enter independentlyAnnotatedCriterionScores, callerCalibratedCriterionWeights, criterionKnownFlags, using the field meanings and rules below, then run the tool.
- Read result.knownWeightedScore, result.unknownWeightFraction, result.unknownCriterionIndices. Use the units and sign conventions below. Review unresolved records and any possible bounds separately from known results.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| independentlyAnnotatedCriterionScores | Independently annotated criterion scores | list of number | Required |
| callerCalibratedCriterionWeights | Caller calibrated criterion weights | list of number | Required |
| criterionKnownFlags | Criterion known flags; binary 1 means the matched observation is known, 0 means unknown | list of number | Required |
Require exactly three aligned criterion scores in 0–100, nonnegative weights and binary known flags. Total weight and known-item weight must both be positive. Return the weighted mean normalized over known items, unknown weight fraction of all weight and zero-based unknown indices. Despite the title, the returned score is a weighted mean rather than an unnormalized sum. Unknown scores must still meet the bounds.
knownScore=Σ(score_i×weight_i×known_i)/Σ(weight_i×known_i); unknownWeightFraction=Σ(weight_i×(1−known_i))/Σweight_i Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Structured result; its nested fields below contain the actual calculation or selected reference | object |
| result.knownWeightedScore | Weighted mean normalized over known criteria only, on the supplied 0–100 score scale | number |
| result.unknownWeightFraction | Weight of unknown criteria divided by total criterion weight; dimensionless fraction | number |
| result.unknownCriterionIndices | Unknown Criterion Indices; zero-based positions in the corresponding input record; unresolved records kept separate from known results | list |
{
"input": {
"independentlyAnnotatedCriterionScores": [
20,
40,
60
],
"callerCalibratedCriterionWeights": [
1,
2,
1
],
"criterionKnownFlags": [
1,
1,
1
]
},
"output": {
"result": {
"knownWeightedScore": 40,
"unknownWeightFraction": 0,
"unknownCriterionIndices": []
}
}
}Limitations and assumptions
Object condition, observation coverage and applicable references determine applicability. A supplied comparison or record does not certify conservation treatment, engineering, access compliance or insurance eligibility.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related step, use Waverly Criteria Points Quick Scorer: The Waverley criteria concern historical, aesthetic and research significance in a declared assessment context. For a complementary comparison, use Metadata Record Triple Completeness ID-Creator-Date Checker: A metadata triple records an object identifier, creator attribution and creation-date information.
References
Similar or alternative tools
- Waverly Criteria Points Quick Scorer
The Waverley criteria concern historical, aesthetic and research significance in a declared assessment context.
- Metadata Record Triple Completeness ID-Creator-Date Checker
A metadata triple records an object identifier, creator attribution and creation-date information.