# ---------------------------------------------------------------------------
# Product tiers for end-of-line grade assignment.
#
# Boundaries follow the binary split used in the source literature (550 cycles,
# Severson et al. 2019) plus an upper automotive-qualification tier.
#
# RECOMPUTE the realised class balance from data and log it; do not assume.
# Phase 3 must annotate the observed A/B/C counts on the cycle-life
# distribution figure. If a tier turns out to be nearly empty, the boundary is
# a modelling liability (a classifier cannot learn a class with five examples)
# and must be revisited here rather than worked around downstream.
# ---------------------------------------------------------------------------

grades:
  A: {min_cycles: 1200, tier: "automotive traction pack"}
  B: {min_cycles: 550,  tier: "stationary ESS / second tier"}
  C: {min_cycles: 0,    tier: "scrap or non-critical"}

# Minimum cycle life for any shipped application. A cell below this is not
# saleable into either tier, so this coincides with the A/B/C floor at the
# B boundary by construction.
warranty_target_cycles: 550

# Ordering is load-bearing: it defines which off-diagonal cells of the cost
# matrix are escapes (assigned tier more demanding than true grade) and which
# are overkill. Listed most-demanding first.
grade_order: [A, B, C]

# Grades that constitute "shipped" product. Escape rate is defined over these
# only: a cell scrapped as C cannot escape into the field by definition.
shipped_grades: [A, B]

labelling:
  # Cycle life is defined as the cycle at which discharge capacity first falls
  # below eol_capacity_fraction * nominal (see configs/data.yaml). Grades are
  # assigned by thresholding that observed value.
  #
  # Boundary handling: a cell with cycle life exactly equal to a boundary is
  # assigned to the HIGHER tier (>= comparison). Stated explicitly because an
  # off-by-one at the boundary silently moves cells between classes and would
  # make the class balance irreproducible.
  boundary_inclusive: true