Planck 2015 results XI. CMB power spectra, likelihoods, and robustness of parameters
Bibliographic record
Abstract
The Planck Collaboration acknowledges the support of:\n\t\t\t\t ESA; CNES, and CNRS/INSU-IN2P3-INP (France); ASI, CNR, and INAF\n\t\t\t\t (Italy); NASA and DoE (USA); STFC and UKSA (UK); CSIC, MINECO, JA\n\t\t\t\t and RES (Spain); Tekes, AoF, and CSC (Finland); DLR and MPG (Germany);\n\t\t\t\t CSA (Canada); DTU Space (Denmark); SER/SSO (Switzerland); RCN (Norway); SFI (Ireland); FCT/MCTES (Portugal); ERC and PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which\n\t\t\t\t technical or scientific activities they have been involved in, can be found at\n\t\t\t\t http://www.cosmos.esa.int/web/planck/planck-collaboration. We\n\t\t\t\t further acknowledge the use of the CLASS Boltzmann code (Lesgourgues 2011)\n\t\t\t\t and the Monte Python package (Audren et al. 2013) in earlier stages of this\n\t\t\t\t work. The likelihood code and some of the validation work was built on the library pmclib from the CosmoPMC package (Kilbinger et al. 2011). This research\n\t\t\t\t used resources of the IN2P3 Computer Center (http://cc.in2p3.fr) as well\n\t\t\t\t as of the Planck-HFI DPC infrastructure hosted at the Institut d’Astrophysique\n\t\t\t\t de Paris (France) and financially supported by CNES.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.059 |
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".