Seed Carbonization Condition Score

Compare independently observed seed-preservation features with caller condition requirements. Retains compatible classes and missing evidence; does not determine carbonization temperature, dating integrity or taxonomy.

Description

Seed carbonization interpretation requires independently assessed preservation features.

Seed carbonization interpretation requires independently assessed preservation features.^[1]

Unknown observations should remain candidate evidence rather than become a fabricated condition score. Use Seed Carbonization Condition Score to evaluate the supplied inputs under the model explained below.

When to use Seed Carbonization Condition Score

Use Seed Carbonization Condition Score 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 Seed Carbonization Condition Score and understand its results

Define the candidate classes and their positive-evidence requirements, then distinguish evidence presence from whether it is known.
Enter Caller seed carbonization class ids, Caller seed carbonization requirements, Observed seed carbonization evidence, Known seed carbonization 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
callerSeedCarbonizationClassIds	Caller seed carbonization class ids	array of string	Required; constraints below
callerSeedCarbonizationRequirements	Caller seed carbonization requirements	array of array	Required; constraints below
observedSeedCarbonizationEvidence	Observed seed carbonization evidence	array of number	Required; constraints below
knownSeedCarbonizationFlags	Known seed carbonization 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": {
    "callerSeedCarbonizationClassIds": [
      "a",
      "b"
    ],
    "callerSeedCarbonizationRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedSeedCarbonizationEvidence": [
      1,
      0
    ],
    "knownSeedCarbonizationFlags": [
      1,
      0
    ]
  },
  "output": {
    "compatibleCallerClasses": [
      "a",
      "b"
    ],
    "fullyWitnessedCallerClasses": [
      "a"
    ]
  }
}

Limitations and assumptions

Unknown observations should remain candidate evidence rather than become a fabricated condition score. 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 Burning Stage Bone Color Classifier for a related task: Bone burning interpretation depends on more than color alone.

Use Charcoal Ring Porosity Classifier for a related task: Charcoal porosity classification relies on independently annotated anatomical evidence.

References

Seed Carbonization Condition Score — tool.io

Similar or alternative tools

Burning Stage Bone Color Classifier
Bone burning interpretation depends on more than color alone.
Charcoal Ring Porosity Classifier
Charcoal porosity classification relies on independently annotated anatomical evidence.