MétaCan
Menu
← Back to cohort
Record W4412121271 · doi:10.5194/epsc-dps2025-121

Bioenergetic Modeling of Methanogens in Europa's Subsurface Ocean Environment

2025· preprint· en· W4412121271 on OpenAlexaff
Maximos Goumas, Peter M. Higgins, Manasvi Lingam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBioenergeticsAstrobiologyEnvironmental scienceOceanographyGeologyChemistryBiology

Abstract

fetched live from OpenAlex

The ultimate goal for many is to find life elsewhere in the universe, whether it be in our own Solar System or further, but current technological, physical, and/or other limitations prevent a definitive answer. Furthermore, the surface and subsurface oceans on the icy moons of our Solar System as well as on exoplanets, such as Hycean worlds, are manifestly of great astrobiological interest. Modeling these environments and the growth of putative organisms within them can aid in this grand endeavor of understanding and identifying other habitable and inhabited worlds. Simulating the interactions of these organisms with each other and with the available environmental nutrients and substrates, as well as the accessible energy sources and sinks, is crucial for not only determining the habitability potential of such environments but also developing a theoretical framework for later use during comparisons with direct observation and data collection. To elaborate on this theme further, ascertaining putative properties of ecosystems from a bioenergetic standpoint is valuable for the following two reasons: (1) interpretation and analysis of data from future missions, such as Europa Clipper and JUICE, and (2) theoretical predictions of what to expect in these ecosystems, thus potentially aiding in selecting the design and functionality of future missions and instruments. In this study, modeling is achieved through use of the python code package NutMEG (Nutrients, Maintenance, Energy and Growth), in conjunction with The Geochemist's Workbench (referred to as GWB), with the chief objective to simulate hydrogenotrophic methanogens in the ocean environment of Europa, which may be more acidic relative to Earth (among other properties). The initial theoretical composition of Europa's ocean was formed through a literature search of various other models and laboratory experiments. This composition was then used as an input for GWB, where the activities of CO2 and H2O were determined for a range of pH values from 4 to 7, in half-pH increments, and a temperature range of 0 to 200 degrees Celsius, in 10 degree increments. These activities, along with the theoretical composition of Europa's ocean and the chosen temperature and pH ranges, were then used as inputs to NutMEG where the metabolic and environmental chemical reactions were simulated to determine bioenergetic habitability of Europa's subsurface ocean. High and low salinity scenarios were also tested to determine the power supply available and whether the power available would meet various habitability criteria, including exponential growth of methanogens. The results presented show that the theoretically available maintenance power and specific combinations of lower ocean pH (roughly from 4 to 5.5) and higher temperature meet the criteria for methanogens to survive in a relatively habitable environment. Lower pH and higher temperatures also allow for a lower salinity environment to meet the same habitability criteria. This work will also be expanded to Hycean worlds (which are thought to host global oceans under a thick Hydrogen, and sometimes Helium, atmosphere) and potentially to the early Earth as well, specifically the Hadean-Archean Earth.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.228
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicMethane Hydrates and Related Phenomena→French-language works237,207→