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Record W4403686090 · doi:10.1080/15230430.2024.2407711

Seasonal changes in the viability and abundance of bacterial cells in the snowpack ecosystem of a High Arctic ice cap

2024· article· en· W4403686090 on OpenAlexfundno aff
Archana Dayal, Andy Hodson, Marie Šabacká, Alan Smalley

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
FundersNorges ForskningsrådChina Scholarship CouncilQueen's UniversityCommonwealth Scholarship Commission
KeywordsSnowpackAbundance (ecology)ArcticEcosystemSnowEnvironmental scienceSea iceEcologyPermafrostAtmospheric sciencesOceanographyGeologyBiologyGeomorphology

Abstract

fetched live from OpenAlex

Microbes play an essential role in nutrient turnover within Arctic environments, and their contribution to biogeochemical cycles can depend on several factors, including but not limited to cell viability. In this study, we employed the SYBR-PI dual cell stain to epifluorescence microscopy to enumerate proportions of potentially viable and non-viable bacterial cell populations within a melting snowpack on an ice cap, Foxfonna in Svalbard. Non-viable cells dominated on Foxfonna (2.5 ± 0.36 × 107 cells m−2) during the June to early July period, when biological production was usually at its peak. Furthermore, non-viable cells also dominated the total cell abundance within superimposed ice (223 ± 242 cells mL−1) and glacial ice (695 ± 717 cells mL−1) beneath the snow. We propose that the rapid, early loss of cell viability was caused by a number of abiotic and biotic factors. Hence, necromass (dead cell residue) contributed to the export of organic matter to downstream ecosystems.

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)0.005
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 score0.115
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.296
Teacher spread0.271 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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