Bibliographic record
Abstract
The CESM2 Large Ensemble consists of 100 members at 1 degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. Two separate sets of biomass burning emissions forcing files were used within the ensemble. Members 1-50 were forced with CMIP6 protocols identical to those used in Danabasoglu et al. (2020) in the paper for CESM2. For members 51-100, the most relevant species for biomass burning fluxes from the CMIP6 protocols were smoothed with an 11-year running mean filter, impacting the fluxes over the years 1990-2020.The CESM2 Large Ensemble uses a combination of different oceanic and atmospheric initial states to create ensemble spread as follows: Members 1-10: These begin from years 1001, 1021, 1041, 1061, 1081, 1101, 1121, 1141, 1161, and 1181 of the 1400-year pre-industrial control simulation. This segment of the control simulation was chosen to minimize drift.Members 11-90: These begin from 4 pre-selected years of the pre-industrial control simulation based on the phase of the Atlantic Meridional Overturning Circulation (AMOC). For each of the 4 initial states, there are 20 ensemble members created by randomly perturbing the atmospheric temperature field on the order of -14K. The chosen start dates (model years 1231, 1251, 1281, and 1301) sample AMOC and Sea Surface Height (SSH) in the Labrador Sea at their maximum, minimum and transition states.Members 91-100: These begin from years 1011, 1031, 1051, 1071, 1091, 1111, 1131, 1151, 1171, and 1191 of the 1400-year pre-industrial control simulation. This set includes the extensive "MOAR" output, which can be used to drive regional climate models.The initialization design allows assessment of oceanic (AMOC) and atmospheric contributions to ensemble spread, and the impact of AMOC initial-condition memory on the global earth system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".