Saved evidence report

Fieldwork / Paths

Explore the saved analysis below. Controls filter this report; they do not rerun the analysis or retrieve source data.

Population: MR export slots · 8 evaluated source rows

Search coverage and limits

Search limits restrict what was tested or retained. Untested work is unknown. Display limits below only restrict this report.

paths: 1/1 pairs evaluated

paths: 2/2 features evaluated

features requested2
features evaluated2
features omittednone
pairs evaluated1
pair candidates1
paths evaluated1
distinct alternatives1
alternative policybest_order_per_feature_set_collapsing_alias_substitutions
search exhausted budgetno
requested depth2
returned depth2
Visual summary
Fieldwork pathsFieldwork / Paths8 evaluated rows · saved evidencesite → exam_idObserved prefix groups: site: 2 → exam_id: 4; Supported coarse-to-fine nesting: site → exam_id;Availability nonmodal fractions: 0, 0

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Inspect findings

census pathsite → exam_id

Analysis: rows; presence=per_row

Finding f0 · counting unit: rows

score0.15
prefix counts2, 4
prefix cost0.15
overflow0
nesting inversions0
redundant steps0
alias steps0
target impurity sum0
target impurity by depthnone
availability impurity sum0
availability impurity by depth0.0, 0.0
explanationObserved prefix groups: site: 2 → exam_id: 4; Supported coarse-to-fine nesting: site → exam_id; Availability nonmodal fractions: 0, 0
reasons
  • kindbranching
    dimensionssite, exam_id
    prefix groups2, 4
    overflow cost0
  • kindnesting
    coarse to fine
    • site, exam_id
    reversed edges0
  • kindredundancy
    no new groupsnone
    equivalent pairsnone
  • kindavailability_separation
    nonmodal fraction by depth0.0, 0.0

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 3/8 saved source positions [0, 1, 2]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f0')

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