Bibliographic record
Abstract
This chapter features the USA-based LIGO, the Laser Interferometer Gravitational-Wave Observatory – the first of three case studies covering different worldwide interferometric gravitational wave detectors. In addition to describing the basic interferometer operation and its various components, we discuss the technological challenges that have been overcome for its successful operation. Introduction The prediction of gravitational waves (GWs), oscillations in the spacetime metric that propagate at the speed of light, is one of the most profound differences between Einstein's general theory of relativity and the Newtonian theory of gravity that it replaced. As discussed in Chapter 1, GWs remained a theoretical prediction for more than 50 years until the first observational evidence for their existence came with the discovery and subsequent observations of the binary pulsar PSR 1913+16, by Russell Hulse and Joseph Taylor (Weisberg and Taylor, 2005). In about 300 million years, the PSR 1913+16 orbit will decrease to the point where the pair coalesces into a single compact object, a process that will produce directly detectable gravitational waves. In the meantime, the direct detection of GWs will require similarly strong sources – extremely large masses moving with large accelerations in strong gravitational fields. The goal of LIGO, the Laser Interferometer Gravitational-Wave Observatory (Abramovici et al. , 1992), is just that: to detect and study GWs of astrophysical origin. Achieving this goal will mark the opening of a new window on the Universe, with the promise of new physics and astrophysics. In physics, GW detection could provide information about strong-field gravitation, the untested domain of strongly curved space where Newtonian gravitation is no longer even a poor approximation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.045 |
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".