Bibliographic record
Abstract
We explore the formation and evolution of debris ejected around quark stars in the Quark Nova scenario, and the application to Soft Gamma-ray Repeaters (SGRs) and Anomolous X-ray Pulsars (AXPs). If an isolated neutron star explodes as a Quark Nova, an iron-rich shell of degenerate matter forms from its crust. This model can account for many of the observed features of SGRs and AXPs such as: (i) the two types of bursts (giant and regular); (ii) the spin-up and spin-down episodes during and following the bursts with associated increases in ; (iii) the energetics of the boxing day burst, SGR1806+20; (iv) the presence of an iron line as observed in SGR1900+14; (v) the correlation between the far-infrared and the X-ray fluxes during the bursting episode and the quiescent phase; (vi) the hard X-ray component observed in SGRs during the giant bursts, and (vii) the discrepancy between the ages of SGRs/AXPs and their supernova remnants. We also find a natural evolutionary relationship between SGRs and AXPs in our model which predicts that the youngest SGRs/AXPs are the most likely to exhibit strong bursting. Many features of X-ray Dim Isolated Neutron stars (XDINs) are also accounted for in our model such as, (i) the two-component blackbody spectra; (ii) the absorption lines around 300 eV; and (iii) the excess optical emission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".