# soilKey benchmark suite -- v0.9.110

Generated by `run_all_benchmarks()` (max_n = 200, level = order).

## Accuracy by dataset x system

Headline metric for imbalanced classes is **balanced accuracy / macro-F1**, read against the **NIR** (no-information-rate) majority-class baseline. Point accuracy carries a bootstrap 95% CI.

| Dataset | System | n | Accuracy [95% CI] | Bal. acc | Macro-F1 | Kappa | NIR | Flag |
|---------|--------|--:|-------------------|---------:|---------:|------:|----:|------|
| canonical | all | 132 | 100.0% | n/a | n/a | n/a | n/a |  |
| febr | sibcs | 200 | 38.0% (31.5%-45.0%) | 23.1% | 17.3% | 0.17 | 28.0% |  |
| febr | usda | 194 | 45.4% (38.1%-52.6%) | 28.1% | 25.5% | 0.34 | 32.5% |  |
| febr | wrb2022 | 199 | 18.1% (13.1%-23.6%) | 12.1% | 8.3% | 0.15 | 28.1% |  |
| redape | sibcs | 94 | 59.6% (50.0%-69.1%) | 61.6% | 60.4% | 0.54 | 26.6% |  |

## Zero-recall classes (improvement targets)

- **redape/sibcs**: nitossolos, unknowns

## Notes

- The **canonical** row is an offline fixture sanity check (coverage, not field accuracy); it has no confusion matrix, so its per-class metrics are blank.
- Rows flagged **n<30** are statistically indicative only. External-dataset rows reflect the local data snapshot and `max_n`.
- **lucas_esdb/wrb2022** is a topsoil-only **lower bound** (LUCAS ships 0-20 cm chemistry only); the honest WRB-at-scale number is the morphologically-complete **FEBR** row. For a LUCAS estimate with a synthetic subsoil, run the opt-in (network, ~1 h): `benchmark_lucas_2018(pedons, fill_subsoil_from = "soilgrids")`.
- **kssl/usda** uses a head-N (not random) sample of the gpkg; **bdsolos** accumulates leading (state-clustered) CSVs until the label cap is met. Both are documented samples, not full random draws.
