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Table F.1.
Akaike weights wi for all lines combined, using the continuum fitting algorithm described in Gavel et al. (2019).
| T0: | 5.8 | 5.9 | 5.95 | 6.0 | 6.09 | 6.2 | Null |
|---|---|---|---|---|---|---|---|
| Const. | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Lin. | 0.00 | 0.00 | 0.00 | 0.01 | 0.54 | 0.19 | 0.00 |
| Quad. | 0.00 | 0.00 | 0.00 | 0.00 | 0.13 | 0.06 | 0.00 |
| Cubic | 0.00 | 0.00 | 0.00 | 0.00 | 0.05 | 0.02 | 0.00 |
| 4th | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 5th | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Notes. Weights below 0.01 are written in grey, since they represent models that, under the Bayesian interpretation of Akaike weights, have a probability below 1% of being KL-minimising.
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