Scientific finding (v1.0). Two implementations of the CBM-CFS3 forest carbon model family, libcbm (Canadian Forest Service Python/C++ reimplementation) and GCBM (the moja FLINT spatially explicit engine), are compared across six US states under matched parameter conventions. The year-five carbon density gap is approximately +24%, with libcbm/GCBM ratios between 0.74 and 0.80 in Washington, Minnesota, Indiana, Maine, and Oregon, and a single warm-donor outlier in Georgia at 1.05. Replacing the uniform forest-type stratification used in legacy parity runs with a stratification anchored on FIA EXPNS expansion factors (the canonical FIA Total Area Estimator) adds +6,760 TgC to the CONUS year-five carbon stock at n=39 states, a +14% shift on 246.5 Mha. We conclude that inventory stratification, not engine implementation, dominates cross-model uncertainty for this generation of CBM-CFS3 carbon models.New in v1.1.0: 50-year baseline trajectory at n=48 CONUS states. Under canonical B1.3 FIA EXPNS inventory, libcbm projects a +4,870 TgC net CONUS gain over 50 years (177.2 to 191.3 Mg/ha mean). Forty one of forty eight states are sinks. The Pacific Northwest is the only net regional source (-6.5 Mg/ha mean, driven by California at -17.1 Mg/ha), while the Lake States is the largest gaining region (+42.4 Mg/ha mean). The PNW source signature is consistent with the regional dead organic matter pool fingerprint: cool moist climates that carry the highest libcbm baseline slow soil stocks fail to keep accumulating over the horizon.Regional DOM-pool fingerprint (v1.0). Mean libcbm slow soil carbon under B1.3 FIA EXPNS is 184 Mg/ha in the Pacific Northwest, 143 in the Atlantic Maritime Northeast, and 105 to 119 Mg/ha across the South, Lake States, and Mountain West. A Q10 mean annual temperature sweep on Oregon (MAT 4 to 13 C) shows the slow-soil overshoot is established during spinup rather than emerging from runtime Q10 scaling: libcbm/GCBM stays at 1.35 even at MAT 13 C.Contents. 48-state libcbm year-five pool outputs under canonical B1.3 FIA EXPNS inventory; new in v1.1.0: 48-state libcbm 50-year baseline trajectories under B1.3 FIA EXPNS plus regional and CONUS rollups; 6-state libcbm vs GCBM density gap matrices under both B1.1 v6 parity and B1.3 FIA EXPNS; per-pool and per-DOM-pool decompositions; F3 Q10 MAT sensitivity sweeps for Georgia and Oregon; six publication figures supporting the methods finding (Fig 1 through Fig 6); and the cbm_conus v0.2.1 source tarball with the reproduction pipeline.Pipeline. All libcbm outputs were produced with the GCBM2hpc pipeline (github.com/holoros/GCBM2hpc) on the Ohio Supercomputer Center Cardinal cluster (allocation PUOM0008). Six-state GCBM aggregates were produced spatially via moja FLINT containerized GCBM at 1-degree WGS84 tiles. The CONUS economic and ecosystem services framework that consumes the 50-year baseline is at github.com/holoros/cbm_conus (v0.2.1 archived here).Companion methods note: holoros.github.io/perseus-forest-intelligence/methods/inventory-stratification/