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Record W3112595299 · doi:10.1080/14634988.2020.1831336

Investigation of stalk formation, frequency of dividing cells and gene expression in periphyton mats dominated by <i>Didymosphenia geminata</i>

2020· article· en· W3112595299 on OpenAlexaffabout
Krista M. Larsen, Leland J. Jackson, Sean M. Rogers

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2020
Typearticle
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsSt. Mary's UniversityUniversity of Calgary
Fundersnot available
KeywordsDiatomPeriphytonBiologyThalassiosira pseudonanaPhaeodactylum tricornutumBotanyPhosphorusGene expressionGeneCaulobacter crescentusAlgaeEcologyGeneticsPhytoplanktonNutrientChemistry

Abstract

fetched live from OpenAlex

The diatom Didymosphenia geminata can produce thick benthic mats along river bottoms, possibly in association with soluble reactive phosphorus mat-forming genetic variants, or gene expression associated with environmental variation. We isolated cells collected from mat-forming and non-mat-forming sites in British Columbia and Alberta, Canada and compared soluble reactive phosphorus, frequency of dividing cells and gene expression via RNA sequencing. Frequency of dividing cells and soluble reactive phosphorus were higher at the non-mat-forming site and there was no significant difference in frequency of dividing cells between Alberta and British Columbia sites, with one exception. Sixty-six gene products from reference diatom (Phaeodactylum tricornutum and Thalassiosira pseudonana) and bacterial (Caulobacter crescentus) genomes were expressed only in the mat-forming sample and many were related to phosphorus metabolism. One hundred seventy two genes were expressed only in the non-mat-forming sample and many were related to cell division and silica metabolism. Differential gene expression in the periphyton communities suggests that there might be different periphyton molecular phenotypes, including D. geminata, related to P acquisition and polysaccharide metabolism at the low P site and cell division and growth at the high P site.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.255
Teacher spread0.234 · 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 designBench or experimental
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
Published2020
Admission routes2
Has abstractyes

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