Imperforate Status Flag Checker
Separate no observed perforations from incomplete inspections and original imperforate authenticity. 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
Perforation status depends on what was observed along all four edges.
Perforation status depends on what was observed along all four edges.1
This checker distinguishes observed perforations from a fully assessed absence of holes and an incomplete edge assessment.
When to use Imperforate Status Flag Checker
Use this tool when you need to summarize the supplied collecting records or matched observations. Prepare independently verified observations, reference edition/context, and matching units or currency basis.
How to use Imperforate Status Flag Checker and understand its results
- Prepare independently verified observations, reference edition/context, and matching units or currency basis.
- Enter observedEdgeHoleCounts, edgeInspectionKnown, following the field table and calculation rules below, then run the tool.
- Read status. 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 |
|---|---|---|---|
| observedEdgeHoleCounts | Observed edge hole counts | list of number | Required |
| edgeInspectionKnown | Edge inspection known | list of number | Required |
Require exactly four nonnegative safe-integer edge hole counts and four binary known flags. Any known positive count returns observed-perforation; otherwise all known flags return no-observed-perforation, and remaining unknown flags return unknown. An absence of observed holes does not prove original imperforate manufacture.
status=anyKnownPositiveHole?observed-perforation:allKnown?no-observed-perforation:unknown Read these symbols with the field meanings and branch or reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| status | Status | string |
{
"input": {
"observedEdgeHoleCounts": [
0,
0,
0,
0
],
"edgeInspectionKnown": [
1,
1,
1,
0
]
},
"output": {
"status": "unknown"
}
}Limitations and assumptions
Observations, reference edition, and applicability are independently supplied. Arithmetic and reference summaries do not provide authentication, a certified grade, or an actual market price.
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.
Alternative or Complementary methods
For a related task, use Sound Stamp Binary Quality Gate Checker: A sound-stamp assessment depends on which faults were actually inspected. This gate distinguishes an observed fault, a fully assessed absence of the listed faults, and an incomplete assessment. For a complementary step, use Roulette Separation Identifier (no holes): Rouletting separates stamps through small cuts rather than a row of punched perforation holes. This identifier compares supplied evidence with caller-defined class requirements and retains ambiguity where observations are missing.
References
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Imperforate Status Flag Checker — tool.io
Similar or alternative tools
- Sound Stamp Binary Quality Gate Checker
A sound-stamp assessment depends on which faults were actually inspected. This gate distinguishes an observed fault, a fully assessed absence of the listed faults, and an incomplete assessment.
- Roulette Separation Identifier (no holes)
Rouletting separates stamps through small cuts rather than a row of punched perforation holes. This identifier compares supplied evidence with caller-defined class requirements and retains ambiguity where observations are missing.