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Record W2165513401 · doi:10.1139/w08-117

Microbial genesis, life and death in glacial iceThis article is one of a selection of papers in the Special Issue on Polar and Alpine Microbiology.

2009· review· en· W2165513401 on OpenAlexvenueno aff
P. B. Price

Bibliographic record

VenueCanadian Journal of Microbiology · 2009
Typereview
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
FundersOffice of Polar Programs
KeywordsGlacial periodDepurinationIce ageIce nucleusEarly EarthPermafrostAstrobiologyCosmic rayEcologyBiologyChemistryDNAGeneticsPaleontologyPhysicsNucleation

Abstract

fetched live from OpenAlex

Arguments are given that terrestrial RNA and DNA may have originated in a frozen environment more than 4 billion years ago. Scenarios are developed for atmospheric transport of microbes onto glacial ice, their adaptation to subzero temperatures in the ice, and their incorporation into one of three habitats - liquid veins, mineral grain surfaces, or isolated inside 1 of the crystals that make up polycrystalline ice. The Arrhenius dependence of microbial metabolic rate on temperature is shown to match that required to repair damage owing to spontaneous DNA depurination and amino acid racemization. Even for the oldest glacial ice, microbial lifetime is shown not to be shortened by radiation damage from 238U, 232Th, or 40K in mineral dust in ice, by phage-induced lysis, or by penetrating cosmic radiation. Instead, death of those cells adapted to the hostile conditions in glacial ice is probably due to exhaustion of available nutrients. By contrast, in permafrost microbial death is more likely due to alpha-particle radiation damage from U and Th in the soil and rocks intermixed with ice. For residence times in ice longer than a million years, spore formers may be unable to compete in longevity with vegetative cells that are able to repair DNA damage via survival metabolism.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.254
Teacher spread0.235 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2009
Admission routes1
Has abstractyes

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