Are We There Yet? Challenges in Quantifying the Frequency of Earth Analogs in the Habitable Zone
Bibliographic record
Abstract
Abstract Searching for life elsewhere in the universe is one of the most highly prioritized pursuits in astronomy today. However, the ability to observe evidence of Earth-like life through biosignatures is limited by the number of planets in the solar neighborhood with conditions similar to Earth. The occurrence rate of Earth-like planets in the habitable zones of Sun-like stars, η ⊕ , is therefore crucial for addressing the apparent lack of consensus on its value in the literature. Here we present a review of the current understanding of η ⊕ . We first provide definitions for parameters that contribute to η ⊕ . Then, we discuss the previous and current estimated parameter values and the context of the limitations on the analyses that produced these estimates. We compile an extensive list of the factors that go into any calculation of η ⊕ , and how detection techniques and surveys differ in their sensitivity and ability to accurately constrain η ⊕ . Understanding and refining the value of η ⊕ is crucial for upcoming missions and telescopes, such as the planned Habitable Worlds Observatory and the Large Interferometer for Exoplanets, which aim to search for biosignatures on exoplanets in the solar neighborhood.
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 | 0.019 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".