Simulating biosignatures from pre-oxygen photosynthesizing life on TRAPPIST-1e
Bibliographic record
Abstract
ABSTRACT In order to assess observational evidence for potential atmospheric biosignatures on exoplanets, it will be essential to test whether spectral fingerprints from multiple gases can be explained by abiotic or biotic-only processes. Here, we develop and apply a coupled 1D atmosphere-ocean-ecosystem model to understand how primitive biospheres, which exploit abiotic sources of H$_2$, CO, and O$_2$, could influence the atmospheric composition of rocky terrestrial exoplanets. We apply this to the Earth at 3.8 Ga and to TRAPPIST-1e. We focus on metabolisms that evolved before the evolution of oxygenic photosynthesis, which consume H$_2$ and CO and produce potentially detectable levels of CH$_4$. O$_2$-consuming metabolisms are also considered for TRAPPIST-1e, as abiotic O$_2$ production is predicted on M-dwarf orbiting planets. We show that these biospheres can lead to high levels of surface O$_2$ (approximately 1–5 per cent) as a result of CO consumption, which could allow high O$_2$ scenarios, by removing the main loss mechanisms of atomic oxygen. Increasing stratospheric temperatures, which increases atmospheric OH can reduce the likelihood of such a state forming. O$_2$-consuming metabolisms could also lower O$_2$ levels to around 10 ppm and support a productive biosphere at low reductant inputs. Using predicted transmission spectral features from CH$_4$, CO, O$_2$/O$_3$, and CO$_2$ across the hypothesis space for tectonic reductant input, we show that biotically produced CH$_4$ may only be detectable at high reductant inputs. CO is also likely to be a dominant feature in transmission spectra for planets orbiting M-dwarfs, which could reduce the confidence in any potential biosignature observations linked to these biospheres.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".