Bibliographic record
Abstract
A concordance of observations indicates that around 80% of the matter in the universe is some unknown dark matter. This dark matter could be comprised of a single structureless particle, but much richer theories exist. Signals from the DAMA, CoGeNT, and CDMS-II dark matter detectors along with the non-observation of dark matter by other detectors motivate theories of composite dark matter along with a “dark” electromagnetic sector. The composite models propose baryon-like or atom-like dark matter. If photons kinetically mix with the “dark” photons, then light traveling through dark matter will experience dispersion. We expect the dispersion to be approximated by the Drude-Lorentz model where the model parameters are particular to a given dark matter candidate. As light travels through the dispersive medium, it can accrue to a frequency-dependent time lag. Measurement of such a time lag can yield clues as to the nature of the dark matter. As a first application, we model hydrogenic dark atoms and use astrophysical data to constrain the mass, binding energy, and the fractional electric charge of the dark atoms.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".