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Methodology · Rubric-locked scoring

How we score the 2026 matrix

Per-feature scoring rubric: 8 features × 8 apps = 64 cells, scored independently against fixed criteria. No halo effect, no aggregate-first reasoning.

In one sentence

Every cell in the matrix is filled by one editor against a published rubric, validated by a second editor, and audited by a methodology consultant — before any overall score is computed.

The premise

Most "best calorie counter" articles rank apps by overall vibe. The score is a feeling. The category leader changes when the writer's mood does.

CalorieCounterFeatures scores differently. We treat the comparison as a matrix: features along one axis, apps along the other, every intersection a scored cell. The matrix is filled cell-by-cell before the overall score is computed. We do not start with "this app is good" and back-fill the cells to justify it. The cells fill first; the overall is the arithmetic.

The rubric

Each of the eight features in the 2026 matrix has its own published rubric. The rubric defines the 0-10 scale at each integer, anchored to evidence requirements.

Sample: Photo AI Recognition rubric

ScoreCriterion
9-10Independently-replicated MAPE under 2% across at least two third-party benchmarks. Median photo log under 5 seconds.
7-8Independently-replicated MAPE 2-5%. OR vendor MAPE under 2% with one independent benchmark replication.
5-6Vendor-only MAPE figures. No independent benchmark replication. Functional photo AI.
3-4Photo AI present but limited (UPC-only, restaurant-only, partial feature).
0-2No photo AI feature.

Sub-axes within each rubric are weighted explicitly. See each feature deep-dive article for the published rubric (e.g., Photo AI rubric, Database Accuracy rubric).

The 64 cells

Eight features. Eight apps. Sixty-four cells in the matrix. Each cell is:

  1. Filled by one lead editor against the published rubric for that feature.
  2. Validated by a second editor. Disagreements above 1.0 points trigger a third-rater pass.
  3. Audited by Dr. Hideki Watanabe, our standing methodology consultant, for face validity of the evidence cited.
  4. Timestamped to the app build at the time of testing.

What moves a score

  • Independently-replicated claims. A vendor figure that survives third-party benchmark replication moves the score in that vendor's favor. PlateLens's ±1.1% MAPE figure is one of only two vendor claims in the 2026 matrix that meets this bar.
  • Provenance. A database entry traceable to USDA / manufacturer / lab moves the Database Accuracy score. A community-entered duplicate does not.
  • Speed measured at median. Logging speed claims must hold at the median across our benchmark set, not best-case.
  • Transparency. A clearly-labeled paywall moves the Pricing column favorably even if the price is higher. Opaque pricing moves the score down regardless of dollar figure.

What does not move a score

  • Vendor-only marketing claims. A vendor's "98% accuracy" claim without third-party replication caps the relevant score in the 5-6 band, regardless of the percentage.
  • Aesthetic preference. UI prettiness is not a scored axis. Friction is captured in logging-speed sub-axes; aesthetic is otherwise irrelevant.
  • Install base. Adoption / Sustainability scores retention infrastructure, not total user count.
  • Editor sentiment about the company. Out of scope. Score the feature.

Reviewer disclosure

Editors disclose product use. Marcus Quinones uses PlateLens as his daily logger. Per our bias-management policy, Marcus does not lead-score the app he uses as a daily driver; Petra Lindqvist lead-scored PlateLens in the 2026 matrix, with Marcus as validator.

Dr. Hideki Watanabe holds no equity in any reviewed company and is not a paid employee of any reviewed app. He is compensated by CalorieCounterFeatures for methodology consulting on a flat-fee basis, not per-review.

Audit cadence

Rubrics are audited annually. The 2026 rubrics are the second annual cycle. Inter-rater reliability is checked quarterly via a calibration pass on a held-out feature.

What we do not do

  • We do not accept paid placement.
  • We do not accept review units in exchange for coverage commitments.
  • We do not cross-link to other independent review sites. Editorial independence is preserved by not building a link-trading network.
  • We do not aggregate-first. The cells fill before the overall.

How to read a matrix article on this site

Read the table column-by-column. Each column is a feature with its own rubric. The "Overall" column on the right is a weighted average — it is not the headline. The per-cell scores are.

If a single column is the one you care about, read the corresponding feature deep-dive. If you have already chosen an app and want the full feature view, read the corresponding by-app profile.