Use-Wear Polish Classifier

Compare independently observed polish and microtopography with caller experimental reference criteria. Overlaps and unknown evidence remain explicit; this does not identify tool function or provenance.

Description

Use-wear interpretation combines independently observed surface features with a reference model.

Use-wear interpretation combines independently observed surface features with a reference model.^[1]

Compatible candidates are distinct from fully witnessed classes or a confirmed tool function. Use Use-Wear Polish Classifier to evaluate the supplied inputs under the model explained below.

When to use Use-Wear Polish Classifier

Use Use-Wear Polish Classifier to evaluate the supplied inputs under the model explained below. Define the candidate classes and their positive-evidence requirements, then distinguish evidence presence from whether it is known.

How to use Use-Wear Polish Classifier and understand its results

Define the candidate classes and their positive-evidence requirements, then distinguish evidence presence from whether it is known.
Enter Caller use wear polish class ids, Caller use wear polish requirements, Observed use wear polish evidence, Known use wear polish flags using the field table and validation rules below, then run the tool.
Read Compatible caller classes, Fully witnessed caller classes; interpret the units, signs, ordering and unknown-value placeholders described below.
Correct reported input errors and rerun. Apply successful results according to their specific meanings and check the assumptions before using them in a decision.

Input field	Meaning	Value type	Requirement
callerUseWearPolishClassIds	Caller use wear polish class ids	array of string	Required; constraints below
callerUseWearPolishRequirements	Caller use wear polish requirements	array of array	Required; constraints below
observedUseWearPolishEvidence	Observed use wear polish evidence	array of number	Required; constraints below
knownUseWearPolishFlags	Known use wear polish flags	array of number	Required; constraints below

Supply unique nonblank class IDs, one rectangular binary requirement row per class, and aligned binary observation and known-flag arrays. Each class must require at least one positive witness. Requirement 0 ignores an evidence column; requirement 1 needs a known positive observation for full witnessing. An unknown requirement remains compatible, while a known absent requirement excludes the class. Both returned class lists preserve caller order. No readiness rubric is installed and compatible does not mean fully witnessed or approved.

compatible = no required known absence; fully witnessed = every required witness known and positive

Use the field order, units and branch conditions described above.

Output field	Meaning	Value type
compatibleCallerClasses	Caller classes with no known absence of required positive evidence.	array of string
fullyWitnessedCallerClasses	Caller classes with all required positive evidence known and present.	array of string

json

{
  "input": {
    "callerUseWearPolishClassIds": [
      "a",
      "b"
    ],
    "callerUseWearPolishRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedUseWearPolishEvidence": [
      1,
      0
    ],
    "knownUseWearPolishFlags": [
      1,
      0
    ]
  },
  "output": {
    "compatibleCallerClasses": [
      "a",
      "b"
    ],
    "fullyWitnessedCallerClasses": [
      "a"
    ]
  }
}

Limitations and assumptions

Compatible candidates are distinct from fully witnessed classes or a confirmed tool function. Apply the exact scope and assumptions explained above.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse arrays fail validation. Domain-specific bounds, alignments and supported tokens are explained above; extremely large inputs or outputs can also be rejected. Input errors differ from successful false results, empty match lists and unknown-evidence placeholders.

Alternative or Complementary methods

Use Knapping Floor Refit Cluster Density Calculator for a related task: Refit-cluster density combines independently assigned clusters with a surveyed floor area.

Use Flake Scar Density Core Calculator for a related task: Core scar density relates counts to matched exposed surface areas.

References

Use-Wear Polish Classifier — tool.io

Similar or alternative tools

Knapping Floor Refit Cluster Density Calculator
Refit-cluster density combines independently assigned clusters with a surveyed floor area.
Flake Scar Density Core Calculator
Core scar density relates counts to matched exposed surface areas.