Bibliographic record
Abstract
Improvements in detection technology has enabled researchers to discover interstellar phenomena at an exponential rate; the most enticing of such marvels are black holes. Development of black hole thermodynamics have yielded promising interdisciplinary results. This paper will explore the fundamental concepts pertaining to black holes, and a brief outline of the scientific contributions made by notable theorists in their respective fields. These concepts will be followed by an in-depth discussion concerning the fundamental attributes and characteristics of black holes. One concept that will be thoroughly discussed is black hole radiation, a theory developed heavily by physicist Stephen Hawking, which was predicated on research concerning the anatomy and effects of rotating black holes in the universe. Derivations of formulae relevant to Hawking radiation, black hole entropy, and classical black hole dynamics will be done, as these are imperative to the elucidation of such sophisticated concepts. Furthermore, these formulae will be applied to pre-existing interstellar phenomena in order to understand the significance of the theoretical findings. Ultimately, mathematical models will be put to the test using real black holes. A model will also be generated in a Python environment to visually exemplify Hawking radiation for a Schwarzschild black hole. Developing a cohesive understanding of the thermodynamic concepts related to black holes will prove fruitful in the application of scientific knowledge to an ever-changing world enticed by interstellar phenomena.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".