{
  "title": "Ideal axial dipole reading: a civilian teaching model",
  "data_kind": "constructed values, not measurements",
  "formula": "B_axial = mu0*m/(2*pi*r^3)",
  "units": {
    "B": "tesla; plotted and tabulated in nanotesla",
    "m": "A m^2",
    "r": "m",
    "mu0": "H/m"
  },
  "mu0_used": 1.2566370614359173e-06,
  "mu0_note": "4*pi*1e-7 H/m is an adequate rounded teaching approximation. In the revised SI, mu0 is experimentally determined rather than fixed exactly to this value. These ideal constructed outputs do not justify comparable measurement precision.",
  "assigned_moment_A_m2": 0.01,
  "range_m": [
    0.1,
    1.0
  ],
  "sample_count": 181,
  "step_m": 0.005,
  "orientation": "Positive axial component for an aligned dipole; no off-axis direction is modeled.",
  "applicability": "An ideal point dipole is used. For a finite source, the dipole approximation requires distance much larger than its characteristic dimensions. No source size is assigned, so applicability to a real source over 0.1\u20131 m is not established.",
  "equal_pair": [
    {
      "moment_A_m2": 0.01,
      "distance_m": 0.2,
      "B_nT": 250
    },
    {
      "moment_A_m2": 0.08,
      "distance_m": 0.4,
      "B_nT": 250
    }
  ],
  "interpretation": "Within this scalar ideal model, differing moment and distance can yield the same reading; a single scalar value does not uniquely identify a source.",
  "excluded": [
    "finite source geometry",
    "background fields",
    "noise",
    "off-axis effects",
    "induced magnetization"
  ],
  "not_claimed": [
    "actual measurement",
    "object identification",
    "sensor performance",
    "orbital design"
  ],
  "figure_axes": {
    "left_or_top": "linear distance; logarithmic B",
    "right_or_bottom": "scenario categories; linear B"
  },
  "validation": {
    "endpoint_nT": [
      2000,
      2
    ],
    "doubling_distance_B_ratio": 8,
    "both_pair_values_nT": 250,
    "relative_tolerance": 1e-12,
    "passed": true
  },
  "reproduction": "python reproduce.py; dependencies: numpy, matplotlib"
}