A Model of Habitability Within the Milky Way Galaxy
Bibliographic record
Abstract
We present a model of the galactic habitable zone (GHZ), described in terms of the spatial and temporal dimensions of the Galaxy that may favor the development of complex life. The Milky Way galaxy was modeled using a computational approach by populating stars and their planetary systems on an individual basis by employing Monte Carlo methods. We began with well-established properties of the disk of the Milky Way, such as the stellar number density distribution, the initial mass function, the star formation history, and the metallicity gradient as a function of radial position and time. We varied some of these properties and created four models to test the sensitivity of our assumptions. To assess habitability on the galactic scale, we modeled supernova rates, planet formation, and the time required for complex life to evolve. Our study has improved on other literature on the GHZ by populating stars on an individual basis and modeling Type II supernova (SNII) and Type Ia supernova (SNIa) sterilizations by selecting their progenitors from within this preexisting stellar population. Furthermore, we considered habitability on tidally locked and non-tidally locked planets separately and studied habitability as a function of height above and below the galactic midplane. In the model that most accurately reproduces the properties of the Galaxy, the results indicate that an individual SNIa is ∼5.6× more lethal than an individual SNII on average. In addition, we predict that ∼1.2% of all stars host a planet that may have been capable of supporting complex life at some point in the history of the Galaxy. Of those stars with a habitable planet, ∼75% of planets are predicted to be in a tidally locked configuration with their host star. The majority of these planets that may support complex life are found toward the inner Galaxy, distributed within, and significantly above and below, the galactic midplane.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".