LRUD Passage Sketch Dimension Completeness Checker

Actual LRUDannotated dimensional completeness; not automatically derive surveyed passage area or verify measurements. Actual independently witnessed criterion columns: left dimension evidence present, right dimension evidence present, up dimension evidence present, down dimension evidence present. Unknowns retained; not readiness, authenticity, legality, diagnosis or safety certification.

Description

LRUD means left, right, up and down passage dimensions under the adopted survey frame.

LRUD means left, right, up and down passage dimensions under the adopted survey frame.^[1]

A completeness inventory checks annotated evidence without inferring unrecorded dimensions.

When to use LRUD Passage Sketch Dimension Completeness Checker

Use this tool to obtain witnessedCriteria, knownMissingCriteria, unknownCriteria from the supplied records using the stated calculation. Establish the survey or observation scope, reference frame, units and independently applicable calibration or qualified plan.

How to use LRUD Passage Sketch Dimension Completeness Checker and understand its results

Establish the survey or observation scope, reference frame, units and independently applicable calibration or qualified plan.
Enter independentlyWitnessedCriterionFlags, criterionEvidenceKnownFlags, using the field meanings and rules below, then run the tool.
Read witnessedCriteria, knownMissingCriteria and unknownCriteria separately. Unknown evidence remains unresolved rather than passing or failing the criterion.
Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.

Input field	Meaning	Value type	Requirement
independentlyWitnessedCriterionFlags	Independently witnessed criterion flags	list of number	Required
criterionEvidenceKnownFlags	Criterion evidence known flags; binary 1 means the matched observation is known, 0 means unknown	list of number	Required

Supply one binary observation flag and one binary known flag for each fixed criterion in the table, in exactly that order. A known one enters witnessedCriteria; a known zero enters knownMissingCriteria; an unknown item enters unknownCriteria regardless of its placeholder observation flag. All flags must be zero or one. The tool accepts wholly unknown records and performs no measurement or automatic verification.

Zero-based column	Criterion
0	left dimension evidence present
1	right dimension evidence present
2	up dimension evidence present
3	down dimension evidence present

witnessed={label_i:known_i=1 and flag_i=1}; knownMissing={label_i:known_i=1 and flag_i=0}; unknown={label_i:known_i=0}

Use the matching field units and the exact boundary, ordering and reference rules above.

Output field	Meaning	Value type
result	Qualified observed calculation or explicit reference	object
result.witnessedCriteria	Fixed criterion labels whose flags are known and present.	list
result.knownMissingCriteria	Fixed criterion labels whose flags are known and absent.	list
result.unknownCriteria	Fixed criterion labels whose observations remain unknown.	list

json

{
  "input": {
    "independentlyWitnessedCriterionFlags": [
      1,
      1,
      1,
      1
    ],
    "criterionEvidenceKnownFlags": [
      1,
      1,
      1,
      1
    ]
  },
  "output": {
    "result": {
      "witnessedCriteria": [
        "left dimension evidence present",
        "right dimension evidence present",
        "up dimension evidence present",
        "down dimension evidence present"
      ],
      "knownMissingCriteria": [],
      "unknownCriteria": []
    }
  }
}

Limitations and assumptions

The actual observation coverage, calibration and qualified plan determine applicability. A numerical result does not establish survey accuracy, route suitability, atmosphere conditions or equipment adequacy beyond the declared comparison.

Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries, subject to the narrower lengths and code rules above. Extremely large composite outputs are rejected.

Alternative or Complementary methods

For a related step, use Shot Data Unit Consistency Checker: A survey record's distance and angle tags declare how its numbers are interpreted. For a complementary comparison, use Survey Data CSV Row Completeness Checker: Survey CSV completeness concerns whether the required fields have documented evidence.

References

LRUD Passage Sketch Dimension Completeness Checker — tool.io

Similar or alternative tools

Shot Data Unit Consistency Checker
A survey record's distance and angle tags declare how its numbers are interpreted.
Survey Data CSV Row Completeness Checker
Survey CSV completeness concerns whether the required fields have documented evidence.