{
  "variant_order": [
    "pointwise_dqags",
    "averaged_uniform_dqags",
    "pointwise_fixedop",
    "pointwise_fixedop_reporting",
    "averaged_uniform_fixedop",
    "averaged_uniform_fixedop_reporting",
    "averaged_npairs_fixedop"
  ],
  "adopted": "averaged_uniform_fixedop",
  "published": "pointwise_dqags",
  "dropped": {
    "averaged_npairs_dqags": "cost; the weights question is answered under the operator. Running the adaptive quadrature at ~1000 fine-grid nodes costs ~4x the operator per draw, and this variant would have bought only a second reading of the uniform-vs-npairs contrast that averaged_uniform_fixedop and averaged_npairs_fixedop already give at a quarter the price. Dropped by Cail 2026-09-04. Nothing structural prevents it: add it back to VARIANTS and it runs."
  },
  "reference_covariance": "mockmatched",
  "covariance_draws": {
    "pointwise_dqags": 500,
    "pointwise_fixedop": 500,
    "pointwise_fixedop_reporting": 500,
    "averaged_uniform_dqags": 500,
    "averaged_uniform_fixedop": 500,
    "averaged_npairs_fixedop": 500
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  "scale_cut_convention": "grid_bin_edges",
  "theta_cut": [
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  "window_bins": [
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    15
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      "integration_limits": "reporting_extent",
      "label": "operator, pointwise (nearest node)",
      "note": "The fourth corner, reachable only by approximation. The single-grid operator evaluates at nodes, so each of the 20 reporting abscissae is snapped to the nearest fine node in log theta -- a shift of up to 0.32%, recorded per point in snap_offsets and carried into the mode provenance. Completes the method x geometry square, so the quadrature and geometry effects can each be read along two edges rather than one. Read it alongside its offsets: the snap moves theta by the same order as the small-theta structure being measured.",
      "reference_covariance": "mockmatched",
      "covariance_draws": 500
    },
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      "covariance_from": "averaged_uniform_fixedop",
      "integration_method": "fixed_operator",
      "geometry": "averaged",
      "rebinning_weights": "uniform",
      "integration_limits": "fine_grid_extent",
      "label": "operator, averaged (uniform), reporting local term",
      "note": "The averaged geometry's counterpart of pointwise_fixedop_reporting, and the fourth reading of the local-term axis. Integral term exactly averaged_uniform_fixedop's -- the operator at every fine node carrying the bin's support, uniformly averaged -- but the local 0.5*(xi_p +/- xi_m) term is the 20-bin reporting measurement rather than the uniform mean of the fine xi over the bin. Those two differ because the reporting xi is its own TreeCorr measurement (pair weighted within the bin), not a rebinning of the fine one. Note the MC covariance chains construct their reporting draws BY uniformly rebinning the fine draws, so there the two local terms coincide by construction and this variant's covariance matches averaged_uniform_fixedop's; the difference lives in the data and mock DATA VECTORS. That is why covariance_from aliases it rather than rebuilding: propagating 500 draws through a transform that provably returns the same covariance is a fake dependency (Cail, 2026-09-06). Measured: substituting the reporting local term on a uniformly-rebinned draw moves the modes by 1.4e-20, against 1.8e-06 on the real reporting measurement. The cost is that the borrowed bundle carries the DONOR's data vector, so this variant has no data PTE; its signal is the mock <xi^B> shift, which comes from mock_pure_eb_modes_scan and is unaffected.",
      "reference_covariance": "mockmatched",
      "covariance_draws": 500
    },
    "averaged_npairs_fixedop": {
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