Window Effect Depth Check for Cabs

Measured normalized central transmitted-light patch under caller geometry/protocol windowing criteria, not critical-angle-only predicted appearance. Missing evidence retained as explicit indices, not confirmed zero margin. Caller independently establishes geometry, assay, calibration and protocol; not material identity, laboratory grade, treatment or care safety.

Description

A central window is an optical appearance observed under a defined illumination and viewing geometry.

A central window is an optical appearance observed under a defined illumination and viewing geometry.^[1]

Comparing measured transmission with caller bounds screens those observations without ray tracing or a universal gemstone cutoff. Use this tool to calculate window effect depth check for cabs from the supplied measurements and assumptions.

When to use Window Effect Depth Check for Cabs

Use this tool to calculate window effect depth check for cabs from the supplied measurements and assumptions. Establish the specimen, measurement protocol, units, geometry and any caller calibration or review limits.

How to use Window Effect Depth Check for Cabs and understand its results

Establish the specimen, measurement protocol, units, geometry and any caller calibration or review limits.
Enter observedCentralWindowTransmission, callerCentralWindowTransmissionLower, callerCentralWindowTransmissionUpper, knownCentralWindowTransmissionFlags, using the field meanings and rules below, then run the tool.
Read signedKnownMargins, knownReviewIndices, unknownIndices. Interpret the fields using their units, signs and evidence conventions below.
If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.

Input field	Meaning	Value type	Requirement
observedCentralWindowTransmission	Observed central window transmission	list of number	Required
callerCentralWindowTransmissionLower	Caller central window transmission lower	list of number	Required
callerCentralWindowTransmissionUpper	Caller central window transmission upper	list of number	Required
knownCentralWindowTransmissionFlags	Known central window transmission flags; binary 1=known, 0=unknown, in the order described below	list of number	Required

Enter nonempty aligned measurements, lower bounds, upper bounds and binary known flags. Each upper bound must be at least its lower bound, including equal bounds. For known observations calculate the smaller of measured−lower and upper−measured; an outside observation has a negative margin and its zero-based index enters knownReviewIndices. Equality is inside the window. Unknown observations receive placeholder margin 0 and their indices enter unknownIndices; that 0 does not mean an observed pass. Bounds are caller inputs, with no installed optical threshold or ray-tracing model.

margin_i=known_i ? min(observed_i−lower_i,upper_i−observed_i) : 0; review={i:known_i and (observed_i<lower_i or observed_i>upper_i)}

Use the exact units, boundary rules, ordering and missing-evidence conventions above.

Output field	Meaning	Value type
signedKnownMargins	Per-item smaller distance to either bound; negative outside, zero placeholder for unknown items	list of number
knownReviewIndices	Zero-based indices of known observations strictly outside the supplied inclusive window	list of values
unknownIndices	Zero-based indices of observations marked unknown	list of number

json

{
  "input": {
    "observedCentralWindowTransmission": [
      2,
      5
    ],
    "callerCentralWindowTransmissionLower": [
      1,
      1
    ],
    "callerCentralWindowTransmissionUpper": [
      3,
      4
    ],
    "knownCentralWindowTransmissionFlags": [
      1,
      0
    ]
  },
  "output": {
    "signedKnownMargins": [
      1,
      0
    ],
    "knownReviewIndices": [],
    "unknownIndices": [
      1
    ]
  }
}

Limitations and assumptions

Comparing measured transmission with caller bounds screens those observations without ray tracing or a universal gemstone cutoff. Interpretation depends on the units, sampling scope and exact model described above.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.

Alternative or Complementary methods

Use Nail-Head Proportion Flag Calculator for this related task: A nail-head appearance is an observed dark central region under a specified viewing protocol.

Use Fish-Eye Detection Depth Check when its separate inputs and assumptions fit the question: A fish-eye appearance is a particular observed optical feature under a defined viewing setup.

References

Window Effect Depth Check for Cabs — tool.io

Similar or alternative tools

Nail-Head Proportion Flag Calculator
A nail-head appearance is an observed dark central region under a specified viewing protocol.
Fish-Eye Detection Depth Check
A fish-eye appearance is a particular observed optical feature under a defined viewing setup.