Cinderella Status Exclusion Flag Checker
Compare caller purpose/franking evidence against independently verified exclusion classes. Independently established positive evidence requirements; overlapping compatible classes and unknowns retained. Caller independently verifies observations, reference edition, units and applicability; not a grading-service certificate, authentication, conservation treatment, actual market price or legal/insurance entitlement.
Description
Cinderella status is a collecting classification whose scope depends on purpose and reference context.
Cinderella status is a collecting classification whose scope depends on purpose and reference context.1
This checker compares supplied evidence classes rather than deciding postal validity or authenticating an item.
When to use Cinderella Status Exclusion Flag Checker
Use this tool when you need to compare explicit positive evidence requirements while keeping compatible and fully witnessed classes separate. Prepare independently verified observations, reference edition/context, and matching units or currency basis.
How to use Cinderella Status Exclusion Flag Checker and understand its results
- Prepare independently verified observations, reference edition/context, and matching units or currency basis.
- Enter callerCinderellaPurposeClassIds, callerCinderellaPurposeRequirements, observedCinderellaPurposeEvidence, knownCinderellaPurposeFlags, following the field table and calculation rules below, then run the tool.
- Read compatibleCallerClasses, fullyWitnessedCallerClasses. For result objects, inspect the nested output fields below, including any missing evidence or selected indices.
- Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| callerCinderellaPurposeClassIds | Caller cinderella purpose class ids | list of string | Required |
| callerCinderellaPurposeRequirements | Caller cinderella purpose requirements | numeric matrix | Required |
| observedCinderellaPurposeEvidence | Observed cinderella purpose evidence | list of number | Required |
| knownCinderellaPurposeFlags | Known cinderella purpose flags; binary 1 means assessed and 0 means unknown. | list of number | Required |
Prepare unique nonblank caller class IDs and a nonempty rectangular binary requirement matrix, with rows aligned to classes and columns to observed evidence and known flags. Every class must require at least one positive feature. A one requires positive evidence; zero ignores the feature and does not require its absence. Both evidence and known flags must be binary. Known required evidence equal to zero excludes a class; unknown required evidence leaves it compatible but unwitnessed. Return every compatible class and the subset for which all required positive evidence is known and present. Requirements can overlap and do not imply a single winner.
compatible_c=∀j(requirement_cj=0 or known_j=0 or evidence_j=1); witnessed_c=∀j(requirement_cj=0 or (known_j=1 and evidence_j=1)) Read these symbols with the field meanings and branch or reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| compatibleCallerClasses | All supplied classes with no contradiction in known required positive evidence, preserving reference order. | list of string |
| fullyWitnessedCallerClasses | Compatible supplied classes whose required positive evidence is entirely known and present. | list of string |
{
"input": {
"callerCinderellaPurposeClassIds": [
"a",
"b"
],
"callerCinderellaPurposeRequirements": [
[
1,
0
],
[
0,
1
]
],
"observedCinderellaPurposeEvidence": [
1,
0
],
"knownCinderellaPurposeFlags": [
1,
0
]
},
"output": {
"compatibleCallerClasses": [
"a",
"b"
],
"fullyWitnessedCallerClasses": [
"a"
]
}
}Limitations and assumptions
Class definitions and evidence observations must be independently established for the relevant issue and reference context. Compatibility with incomplete evidence is not a positive identification, and fully witnessed classes satisfy only the supplied requirements.
Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Arrays must be dense, with no missing cells. Numeric lists allow at most 10000 entries, subject to smaller tool-specific bounds above. Ordinary string lists allow at most 128 strings of at most 128 UTF-16 units each. Numeric matrices allow at most 128 rows and 128 entries per row, with exact shape requirements stated above.
Alternative or Complementary methods
For a related task, use Revenue Stamp versus Postal Usage Classifier: Revenue and postal uses depend on the issue’s purpose, markings, and applicable period. This classifier compares supplied evidence against explicit caller classes instead of determining fiscal or postal validity from a title. For a complementary step, use Postal Stationery Cut Square versus Cut Entire Classifier: Postal stationery may be represented as an entire item or a cut-square portion, with context-dependent classification. This classifier compares explicit observations against caller evidence requirements without inspecting a physical item.
References
-
Cinderella Status Exclusion Flag Checker — tool.io
Similar or alternative tools
- Revenue Stamp versus Postal Usage Classifier
Revenue and postal uses depend on the issue’s purpose, markings, and applicable period. This classifier compares supplied evidence against explicit caller classes instead of determining fiscal or postal validity from a title.
- Postal Stationery Cut Square versus Cut Entire Classifier
Postal stationery may be represented as an entire item or a cut-square portion, with context-dependent classification. This classifier compares explicit observations against caller evidence requirements without inspecting a physical item.