Bibliographic record
Abstract
This thesis presents new 450$\mu$m and 850$\mu$m observations of NGC3627 taken with the new SCUBA-2 with the main goal of trying to better understand the properties of gas and dust in the interstellar medium of NGC3627. We determined properties of the cold component of NGC3627's spectral energy distribution (SED) using dust models given by the Planck Collaboration, by Li and Draine, and allowing the emissivity index to be treated as a free parameter. Fitting the SED required the use of 100$\mu$m, 160$\mu$m, 250$\mu$m, 350$\mu$m, and 500$\mu$m data from the KINGFISH survey. Each of the KINGFISH observations have been passed through an extended emission filter in order to match the SCUBA-2 observations. The best fit temperatures and emissivity indices agreed with the results found in other recent studies, but our fitted masses were smaller than those of other studies due to differences in the fitted temperature and observed fluxes. After the properties of the dust emission were calculated, we implemented a method to determine the amount of molecular hydrogen present in NGC3627. The method we used involves finding a CO-to-H$_2$ conversion factor that minimizes the scatter present in dust-to-gas mass ratio. We used CO J=2-1 from the HERACLES survey and CO J=1-0 from the Nobeyama 45-m telescope to act as our molecular tracer, and HI observations of NGC3627 from the THINGS survey. The results from minimizing the dust-to-gas ratio scatter give low $\alpha_{CO}$ values, that are normally associated with U/LIRGs. The low $\alpha_{CO}$ values can be attributed to the treatment of the error associated with reported $\alpha_{CO}$. The uncertainties for $\alpha_{CO}$ reported in this thesis are a minimum estimate, and if the error associated with $\alpha_{CO}$ is large enough, then the best fit $\alpha_{CO}$ values can be considered as a lower threshold for the system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".