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Mixed light photoperiod and biocide pollution affect lipid profiles of periphyton communities in freshwater ecosystems

2023· preprint· en· W4386771535 on OpenAlexaff
Nicolás Mazzella, Romain Vrba, Aurélie Moreira, Nicolas Creusot, Mélissa Éon, Débora Millan-Navarro, Isabelle Lavoie, Soizic Morin

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsInstitut National de la Recherche Scientifique
FundersInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementAgence Nationale de la Recherche
KeywordsBiocidePeriphytonEnvironmental chemistryMicrocosmAlgaeLight intensityPhototrophLipidomeAquatic ecosystemBiologyPollutantChemistryEcologyPhotosynthesisBotanyLipid metabolismBiochemistry

Abstract

fetched live from OpenAlex

Environmental factors, such as light intensity and exposure to contaminants, may significantly influence the lipid composition of algae in periphytic communities. In this study, we investigated the joint effects of dodecylbenzyldimethylammonium chloride (BAC 12), as biocide, and light photoperiods on the polar lipidome of a freshwater biofilm. Exposure to BAC 12 in a microcosm experiment increased the heterotrophic compartment, while phototrophic organisms were severely affected, with corresponding shifts in lipid composition. The overall decrease in polyinsaturated fatty acids suggested a significant impact of the biocide on biofilm microalgae. However, it was difficult to distinguish the influence of light from that of contamination, as there was no observable effect of photoperiod on conventional fatty acid determination. Thus, the molecular species compositions of both glycolipids and phospholipids were explored in additional multivariate analyses. The results suggested that certain molecular species can serve as more specific markers of light duration at the biofilm scale, independently of the chemical pressure caused by other pollutants.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.963

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 integrity0.0000.000
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 head0.028
GPT teacher head0.211
Teacher spread0.184 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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