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Record W3027062342 · doi:10.1128/msphere.00364-20

Modelling Free-Living and Particle-Associated Bacterial Assemblages across the Deep and Hypoxic Lower St. Lawrence Estuary

2020· article· en· W3027062342 on OpenAlexafffundabout
Ting Cui, Travis Dawson, Susan McLatchie, Katherine A. Dunn, Joseph P. Bielawski, David A. Walsh

Bibliographic record

VenuemSphere · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsDalhousie UniversityConcordia University
FundersCanadian Institutes of Health Research
KeywordsEstuaryEcosystemEnvironmental changeClimate changeHabitatBaseline (sea)EcologyEnvironmental resource managementEnvironmental scienceGeographyOceanographyFisheryBiology

Abstract

fetched live from OpenAlex

The Estuary and Gulf of St. Lawrence (EGSL) in eastern Canada is an appealing ecosystem for studying how microbial communities and metabolic processes are related to environmental change. Ocean and climate variability result in large spatiotemporal variations in environmental conditions and oceanographic processes. The EGSL is also exposed to a variety of additional human pressures that threaten its integrity and sustainable use, including shipping, aquiculture, coastal development, and oil exploration. To monitor and perhaps mitigate the impacts of these human activities on the EGSL, a comprehensive understanding of the biological communities is required. In this study, we provide the first comprehensive view of bacterial diversity in the EGSL and describe the distinct bacterial assemblages associated with different environmental habitats. This work therefore provides an important baseline ecological framework for bacterial communities in the EGSL useful for further studies on how these communities may respond to environmental change.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.680

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.221
Teacher spread0.202 · 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 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

Citations9
Published2020
Admission routes3
Has abstractyes

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