Postal Stationery Cut Square versus Cut Entire Classifier

Compare observed stationery piece edge/extent evidence for caller cut-square/entire categories. 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

Postal stationery may be represented as an entire item or a cut-square portion, with context-dependent classification.

Postal stationery may be represented as an entire item or a cut-square portion, with context-dependent classification.^[1]

This classifier compares explicit observations against caller evidence requirements without inspecting a physical item.

When to use Postal Stationery Cut Square versus Cut Entire Classifier

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 Postal Stationery Cut Square versus Cut Entire Classifier and understand its results

Prepare independently verified observations, reference edition/context, and matching units or currency basis.
Enter callerStationeryCutClassIds, callerStationeryCutRequirements, observedStationeryCutEvidence, knownStationeryCutFlags, 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
callerStationeryCutClassIds	Caller stationery cut class ids	list of string	Required
callerStationeryCutRequirements	Caller stationery cut requirements	numeric matrix	Required
observedStationeryCutEvidence	Observed stationery cut evidence	list of number	Required
knownStationeryCutFlags	Known stationery cut 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

json

{
  "input": {
    "callerStationeryCutClassIds": [
      "a",
      "b"
    ],
    "callerStationeryCutRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedStationeryCutEvidence": [
      1,
      0
    ],
    "knownStationeryCutFlags": [
      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 Imprint Block Presence Bottom Margin Checker: An imprint block includes printed marginal information that must be observed relative to an expected format. This checker compares supplied evidence requirements and returns compatible and fully witnessed classes separately. For a complementary step, use Cinderella Status Exclusion Flag Checker: Cinderella status is a collecting classification whose scope depends on purpose and reference context. This checker compares supplied evidence classes rather than deciding postal validity or authenticating an item.

References

Postal Stationery Cut Square versus Cut Entire Classifier — tool.io

Similar or alternative tools

Imprint Block Presence Bottom Margin Checker
An imprint block includes printed marginal information that must be observed relative to an expected format. This checker compares supplied evidence requirements and returns compatible and fully witnessed classes separately.
Cinderella Status Exclusion Flag Checker
Cinderella status is a collecting classification whose scope depends on purpose and reference context. This checker compares supplied evidence classes rather than deciding postal validity or authenticating an item.