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Record W3201208012

Examination into the HI-SEAS IV built environment reveals differences in the microbial diversity and composition of plastic and wood surfaces

2021· article· en· W3201208012 on OpenAlexaff
Diane Li, Kyle Ching, Wesley Hunt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrobiomeAbiotic componentDiversity (politics)HabitatBeta diversityEcologyBuilt environmentAlpha diversityExtreme environmentMicrobial population biologyComposition (language)AstrobiologyEnvironmental scienceGeographyBiology
DOInot available

Abstract

fetched live from OpenAlex

The conditions within a confined built environment designed for long-term habitation during space travel can influence the microbiomes of the abiotic surfaces, emphasizing the necessity of regular microbial screens. The recent Hawaii Space Exploration Analog and Simulation (HI-SEAS) IV study examined the microbiome of a confined environment built to mimic a habitat on Mars. Temporal variations in microbial diversity were identified within the HI-SEAS built environment, but the factors associated with the observed microbial dynamics had yet to be explored. Here, we identified these factors by investigating the potential effect of resupply events and surface material on microbial diversity and composition. We found that resupply events had no significant effect on the alpha or beta diversity of the microbiome within the HI-SEAS built environment, but that plastic and wood surfaces exhibited significant differences in alpha and beta diversity. Together, our study provides insights into the considerations for monitoring microbial communities within a confined habitat designed for space exploration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.142

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 head0.018
GPT teacher head0.229
Teacher spread0.212 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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