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Record W4408945219 · doi:10.1089/ast.2024.0131

Spectroscopic Protocol for Biosignature Detection: Arctic Ice Samples as Analogs for Icy Moons

2025· article· en· W4408945219 on OpenAlexaboutno aff
Francisco Calapez, Rodrigo Dias, Rute Cesário, Bruno Pedras, João Canário, Zita Martins

Bibliographic record

VenueAstrobiology · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsIcy moonAstrobiologyArcticWater iceThe arcticGeologyRemote sensingAstronomyPlanetPhysicsOceanographySaturn

Abstract

fetched live from OpenAlex

The moons of Jupiter and Saturn, such as Europa and Enceladus, are strong candidates for the search for life outside of Earth. Together with the use of direct observational methods, physical and chemical processes that take place on icy moons may be studied on planetary field analogs, that is, on similar reachable locations on Earth. Fieldwork performed on planetary field analogs can test protocols and technology that may be applied on future space missions to extraterrestrial environments. The Arctic is a strong candidate for such studies. This study assesses a spectroscopic protocol for biosignature detection in the Arctic, as a proxy to icy moons. Samples of ice and the water underneath were collected by our team in different locations at and nearby Hudson Bay, Canada, and spectroscopic analysis detected the presence of humic acid in all the samples. On the contrary, biosignatures such as amino acids and β-carotene may have been present in concentrations below the limit of detection of the equipment used. With proper optimization, it will be possible to implement this simple protocol that relies on lightweight equipment in future space missions to icy moons.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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