HABEBEE: Habitability of Eyeball-Exo-Earths
Bibliographic record
Abstract
Extrasolar Earth and super-Earth planets orbiting within the habitable zone of M dwarf host stars may play a significant role in the discovery of habitable environments beyond Earth. Spectroscopic characterization of these exoplanets with respect to habitability requires the determination of habitability parameters with respect to remote sensing. The habitable zone of dwarf stars is located in close proximity to the host star, such that exoplanets orbiting within this zone will likely be tidally locked. On terrestrial planets with an icy shell, this may produce a liquid water ocean at the substellar point, one particular "Eyeball Earth" state. In this research proposal, HABEBEE: exploring the HABitability of Eyeball-Exo-Earths, we define the parameters necessary to achieve a stable icy Eyeball Earth capable of supporting life. Astronomical and geochemical research will define parameters needed to simulate potentially habitable environments on an icy Eyeball Earth planet. Biological requirements will be based on detailed studies of microbial communities within Earth analog environments. Using the interdisciplinary results of both the physical and biological teams, we will set up a simulation chamber to expose a cold- and UV-tolerant microbial community to the theoretically derived Eyeball Earth climate states, simulating the composition, atmosphere, physical parameters, and stellar irradiation. Combining the results of both studies will enable us to derive observable parameters as well as target decision guidance and feasibility analysis for upcoming astronomical platforms.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".