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Record W3209383065 · doi:10.1111/ele.13893

Low‐salinity transitions drive abrupt microbial response to sea‐level change

2021· letter· en· W3209383065 on OpenAlexaff
Alex Whittle, Robert L. Barnett, Dan J. Charman, Angela Gallego‐Sala

Bibliographic record

VenueEcology Letters · 2021
Typeletter
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsCenter for Northern StudiesUniversité du Québec à Rimouski
FundersNatural Environment Research CouncilSight Research UK
KeywordsEcosystemEnvironmental scienceSalinityEcologyClimate changeSoil salinityOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

The salinisation of many coastal ecosystems is underway and is expected to continue into the future because of sea-level rise and storm intensification brought about by the changing climate. However, the response of soil microbes to increasing salinity conditions within coastal environments is poorly understood, despite their importance for nutrient cascading, carbon sequestration and wider ecosystem functioning. Here, we demonstrate deterioration in the productivity of a top-tier microbial group (testate amoebae) with increasing coastal salinity, which we show to be consistent across phylogenetic groups, salinity gradients, environment types and latitude. Our results show that microbial changes occur in the very early stages of marine inundation, presaging more radical changes in soil and ecosystem function and providing an early warning of coastal salinisation that could be used to improve coastal planning and adaptation.

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)0.001
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0020.003

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 head0.033
GPT teacher head0.245
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 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

Citations16
Published2021
Admission routes1
Has abstractyes

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