bundle/v0/configs/appendix_canary_v2.json

{
  "schema_version": 1,
  "canary_id": "appendix-routes-bounded-v2",
  "evidence_scale": "SCIENTIFIC_CANARY_NOT_CLAIM_ELIGIBLE",
  "coverage_beta": 0.1,
  "learning_rate": 0.0001,
  "coverage_penalty_lambda": 10.0,
  "indicator_eta": 100.0,
  "max_outer_iterations": 2,
  "gradient_clip": [-1000.0, 1000.0],
  "metric_eigenvalue_clip": [0.000001, 1000000.0],
  "finite_difference_step": 0.00001,
  "active_penalty_gradient_check_scale": 1.2,
  "gradient_relative_error_max": 0.001,
  "ot_gradient_relative_error_max": 0.000001,
  "solver_residual_max": 0.0000001,
  "quantile_method": "linear",
  "routes": [
    {
      "name": "empirical_w1_portfolio",
      "task_selector": "portfolio_discrete/d001/r00/n010",
      "wasserstein_order": 1,
      "bootstrap_count": 20,
      "implementation": "appendix_a2_1_type1_independent_socp"
    },
    {
      "name": "absolute_regression",
      "task_selector": "regression_absolute_main/d001/r00/n010",
      "wasserstein_order": 1,
      "bootstrap_count": 20,
      "implementation": "appendix_a3_1_w1_absolute_independent_socp"
    },
    {
      "name": "squared_regression",
      "task_selector": "regression_squared/d001/r00/n010",
      "wasserstein_order": 2,
      "bootstrap_count": 10,
      "implementation": "appendix_a3_2_w2_root_socp_postsolve_square"
    }
  ],
  "paper_scale_deviations": [
    "one task per route only",
    "two outer iterations rather than convergence or one million maximum",
    "no paper-scale uncertainty or claim verdict",
    "local Windows CPU rather than clean Linux"
  ]
}