Lead Service Line Evidence Workbench

A local Python workflow that connects document evidence, material predictions, and traceable human review.

What I did

I developed a third version of the lead-service-line prototype with explicit contracts between page extraction, asset-side linking, model inputs, and reviewer decisions.

Result: The workbench produces a reproducible synthetic demonstration with evidence records, calibrated material predictions, and review queues that can be inspected locally.

Tools

Python
OCR
pandas
scikit-learn
Grouped evaluation
Data validation

The published data and demonstration are synthetic. This is not a deployed utility system, a regulatory determination, or evidence of field accuracy or municipal savings. Real use requires locally verified labels, independent validation, and accountable human review.

Project gallery

Synthetic demonstration figures; these do not show field performance. 1 images. Select an image to view it full size.

Actual V3 synthetic review interface: a 99-record work plan, page-evidence summaries, uncertainty, and reviewer controls. Invented data; no field-performance claim.

Workflow and working demonstration

Inspect a synthetic review work plan

Explore 99 review records from a demonstration run using 480 invented asset-side snapshots. Filter queues, inspect evidence and material probabilities, and record a review decision.

Use invented reviewer IDs and notes. Decisions stay in the browser tab until exported and are not submitted to a server. This demonstration does not establish field performance.

Open interactive demonstration

Six stages from source pages to an accountable review record — Version 3 workflow diagram. Extracted material mentions, verified labels, model predictions, and reviewer decisions remain distinct. The published demonstration uses invented records.

Technical details

Independent project · Version 3 · Synthetic demonstration data

A material mention in a document is not enough to classify a service line. It must refer to the correct property, service side, and time, and unresolved evidence must remain visible.

Preserve page-level text, extraction status, and source identifiers so reviewers can trace a material mention back to its evidence.
Link evidence to an asset and service side, with ambiguous matches retained for review.
Prepare dated snapshots and keep independently verified material labels separate from extracted mentions and model features.
Fit text and structured-data models with separate calibration groups and evaluate on held-out groups.
Use uncertainty and policy checks to create risk, learning, and random-audit queues; a prediction never becomes a verified material label.
Record the configuration, model environment, and output provenance needed to reproduce a local run.

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