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First measurement of Λ+c production down to pT = 0 in pp and p-Pb collisions at √𝑠NN = 5.02 TeV
(2023)
The production of prompt +c baryons has been measured at midrapidity in the transverse momentum interval 0 < pT < 1 GeV/c for the first time, in pp and p–Pb collisions at a center-of-mass energy per nucleon-nucleon collision √sNN = 5.02 TeV. The measurement was performed in the decay channel +c → pK0S by applying new decay reconstruction techniques using a Kalman-Filter vertexing algorithm and adopting a machine-learning approach for the candidate selection. The pT -integrated +c production cross sections in both collision systems were determined and used along with the measured yields in Pb–Pb collisions to compute the pT -integrated nuclear modification factors RpPb and RAA of +c baryons, which are compared to model calculations that consider nuclear modification of the parton distribution functions. The +c /D0 baryon-to-meson yield ratio is reported for pp and p–Pb collisions. Comparisons with models that include modified hadronization processes are presented, and the implications of the results on the understanding of charm hadronization in hadronic collisions are discussed. A significant (3.7σ) modification of the mean transverse momentum of + c baryons is seen in p–Pb collisions with respect to pp collisions, while the pT -integrated +c /D0 yield ratio was found to be consistent between the two collision systems within the uncertainties.
A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.