Iambic Trimeter Porson Law Checker
Audit independently established evidence for Iambic Trimeter Porson Law Checker. Feature columns, independently annotated in order: tragic trimeter independently scanned, long penultimate-foot thesis, word ends after this thesis, monosyllable exception independently witnessed. Unknown evidence and compatible classes retained; not automated semantic, historical, identity, legal or safety determination.
Description
Porson's-law discussion depends on a scanned verse and its word-boundary context.
Porson's-law discussion depends on a scanned verse and its word-boundary context.1
A fixed feature pattern is an audit of independent annotations rather than a full trimeter scanner. Use Iambic Trimeter Porson Law Checker to compare independently annotated features with the fixed rules.
When to use Iambic Trimeter Porson Law Checker
Use Iambic Trimeter Porson Law Checker to compare independently annotated features with the fixed rules. Annotate the ordered features below independently; use separate binary arrays for observed presence and whether each feature is known.
How to use Iambic Trimeter Porson Law Checker and understand its results
- Annotate the ordered features below independently; use separate binary arrays for observed presence and whether each feature is known.
- Enter Observed feature flags, Feature known flags using the field table and validation rules below, then run the tool.
- Read Compatible Classes, Witnessed Classes, Unknown Feature Indices in the Result object; 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 |
|---|---|---|---|
| observedFeatureFlags | Observed feature flags | array of number | Required; constraints below |
| featureKnownFlags | Feature known flags | array of number | Required; constraints below |
Enter aligned binary observed-feature and known-feature arrays in the exact column order below. The array length must match the fixed rule width. In the rule table, 1 requires presence, 0 requires absence and −1 ignores that column. A known observation contradicting a required value excludes a class; an unknown observation leaves it compatible. A witnessed class requires all nonignored columns to be known and equal to their required values. Unknown-feature indices are zero based and lists preserve rule order. The tool does not annotate grammar, interpret a text or independently verify the historical evidence.
compatible = no known rule contradiction; witnessed = every nonignored rule known and matched Use the field order, units and branch conditions described above.
| Zero-based feature index | Independently annotated feature |
|---|---|
| 0 | tragic trimeter independently scanned |
| 1 | long penultimate-foot thesis |
| 2 | word ends after this thesis |
| 3 | monosyllable exception independently witnessed |
| Fixed class | Feature 0 | Feature 1 | Feature 2 | Feature 3 |
|---|---|---|---|---|
| Porson-review-candidate | 1 | 1 | 1 | 0 |
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the named result fields below. | object |
| result.compatibleClasses | Fixed-rule classes with no known contradiction; compatibility can depend on unknown features. | array |
| result.witnessedClasses | Fixed-rule classes whose nonignored requirements are all known and matched. | array |
| result.unknownFeatureIndices | Zero-based indices marked unknown, irrespective of their observed-value placeholders. | array |
{
"input": {
"observedFeatureFlags": [
1,
1,
1,
1
],
"featureKnownFlags": [
1,
1,
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [],
"witnessedClasses": [],
"unknownFeatureIndices": []
}
}
}Limitations and assumptions
A fixed feature pattern is an audit of independent annotations rather than a full trimeter scanner. 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 Dactylic Hexameter Scansion Pattern Checker for a related task: Dactylic hexameter organizes six quantitative feet under a metrical convention.
Use Hendecasyllable Catullus Line Scan Lookup for a related task: A quantitative verse scan needs the text, edition and adopted metrical convention.
References
-
Iambic Trimeter Porson Law Checker — tool.io
Similar or alternative tools
- Dactylic Hexameter Scansion Pattern Checker
Dactylic hexameter organizes six quantitative feet under a metrical convention.
- Hendecasyllable Catullus Line Scan Lookup
A quantitative verse scan needs the text, edition and adopted metrical convention.