# Data audit (P1)
- File: data/model.csv, SHA-256 098123961451e4f9a2e76afdbc83aec78066fc713d0645659c09b77b1636195b (matches bundle.json).
- 10 rows x 3 columns (parameter, value, description). No missing values, no duplicate parameters.
- Values: alpha=0.36, beta=0.95, y_min=0.05, y_max=5.0, grid_points=400, grid_spacing=log, vfi_tolerance=1e-8, eval_y = 0.5, 1.0, 2.0. `value` column mixes numbers and the string "log" (parsed as strings, cast per parameter).
- Units: y and c in units of output; V in utils.
- Anomaly: log step = ln(100)/399 = 0.01154; 0.5, 1.0, 2.0 are NOT grid nodes (e.g. ln(1) lies at index 259.55). Handling: linear interpolation in ln y (allowed by TASK.md), recorded in data_issues.
- Grid adequacy: closed-form steady state y* = (alpha*beta)^(alpha/(1-alpha)) ≈ 0.547 inside grid; optimal y' at y_max=5 is (0.342*5)^0.36 ≈ 1.21 and at y_min=0.05 is ≈ 0.23, both inside grid, so the choice bounds should not bind (verified in code: diag_choice_bound_binds = 0).
- No information-availability violations: only theoretical parameters.
- Exclusions: none (out/exclusions.csv, 10 -> 10).