Erratum: NIHAO IV: core creation and destruction in dark matter density profiles across cosmic time
Bibliographic record
Abstract
In the paper ‘NIHAO IV: Core creation and destruction in dark matter density profiles across cosmic time’ published in MNRAS main journal, Volume 456, Issue 4, p. 3542–3552, an error in table 2 came to our attention. It appears that there is a mismatch between the values on the second line of table 2 and the corresponding curve in fig. 5. Indeed the parameters given for the fit of the relation between α and Mh are incorrect. We realised that the published parameters correspond to a different fitting formula that was used internally during this work and that does not have the right asymptotic behavior when the mass goes to zero. We apologize for this mistake. The correct fitting parameters to reproduce the fitting curve on fig. 5 are given in Table Errata. These parameters were provided only to allow the reader to reproduce our work and therefore this error does not affect our conclusions. Best fit parameters for the value of α computed witihin 1 and 2 % of |$R_{\rm vir}\, \,$|as a function of Mhalo. Best fit parameters for the value of α computed witihin 1 and 2 % of |$R_{\rm vir}\, \,$|as a function of Mhalo.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.044 |
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".