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data <- simulate_gazepoint_governed_data(12L, 4L, 1L, seed = 2701L)
predictors <- c("tracking_ratio", "blink_rate")
clean <- create_gazepoint_synthetic_manifest("quality_status", predictors)
validate_gazepoint_feature_manifest(clean)
#> <gazepoint_feature_manifest_validation>
#> Overall status: PASS
#> Features: 2
#> Non-passing checks: 0
#> status n_checks
#> pass 22
#> review 0
#> fail 0contaminated <- create_gazepoint_feature_manifest(
features = c("tracking_ratio", "outcome_summary"),
scientific_source = c("Synthetic export", "Derived from observed outcome"),
source_table = c("trial_features", "outcome_table"),
transformation = c("Predeclared", "Post-outcome aggregation"),
availability_stage = c("during_exposure", "post_outcome"),
prediction_time_available = c(TRUE, FALSE),
outcome_derived = c(FALSE, TRUE),
post_outcome = c(FALSE, TRUE),
identifier = FALSE,
preprocessing_scope = c("resampling_fold", "none"),
fold_local_required = c(TRUE, FALSE),
reviewer_notes = c("Permitted synthetic feature", "Deliberate contaminated case")
)
validation <- validate_gazepoint_feature_manifest(contaminated)
validation
#> <gazepoint_feature_manifest_validation>
#> Overall status: FAIL
#> Features: 2
#> Non-passing checks: 3
#> status n_checks
#> pass 19
#> review 0
#> fail 3A contaminated manifest must fail or require explicit review before fitting. The example is included to exercise the governance boundary, not to normalize contaminated predictors.
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