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Record W2323982510 · doi:10.2166/wst.2012.387

Automated image analysis of Euglena gracilis Klebs (Euglenophyta) for measuring sublethal effects of three model contaminants

2012· article· en· W2323982510 on OpenAlexaff
I. Netto, Vadim Bostan, Lynda H. McCarthy, Andrew E. Laursen, Kimberley Gilbride, Mehrab Mehrvar, Ronald Pushchak

Bibliographic record

VenueWater Science & Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEuglena gracilisAtrazineBiologyTributyltinCopperChemistryEcologyPesticideBiochemistry

Abstract

fetched live from OpenAlex

The short-term impacts of atrazine (herbicide), tributyltin (organometal) and copper on the behaviour of Euglena gracilis Klebs (Euglenophyta) were assessed. First, the ECOTOX automated image analysis system was used, which measured swimming velocity, cell shape, percentage of cells swimming upwards, and randomness of swimming. Next, visual observation by microscopy was used to measure percentage of cell motility and cell shape. Behavioural changes can be used as an indicator of stress in less than 24 h, potentially making them suitable for inclusion in early-warning systems for water quality. Findings indicate that E. gracilis is a very sensitive organism to copper, showing inhibition of motility with visual observation at 0.8 μmol/L within 1 h. The image analysis system was in general less sensitive than visual observation for detecting behavioural changes after incubation in copper. In contrast, after exposure to organic contaminants atrazine and tributyltin, the ECOTOX system detected small changes in the number of cells swimming upwards (antigravitactic behaviour) at higher concentrations.

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 score0.176
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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