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Record W4242734373 · doi:10.32920/ryerson.14655612.v1

Evaluation of the Multispecies Freshwater Biomonitor to Determine Behavioural Effects of Tributyltin and Atrazine on Daphnia Magna and Hyalella Azteca

2021· preprint· en· W4242734373 on OpenAlexaff
Vivian E. Fleet

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHyalella aztecaDaphnia magnaDaphniaEnvironmental scienceTributyltinEnvironmental chemistryBiotaBiologyContaminationWater qualityEcologyAmphipodaChemistryToxicityCrustacean

Abstract

fetched live from OpenAlex

The Multispecies Freshwater Biomonitor (MFB) has been identified as a system able to continuously monitor water quality through detection of changes in the movements of biota which may be caused by external stressors. In this study, behavioural changes of Daphnia magna and Hyalella azteca when exposed to tributyltin and atrazine were detected using the MFB. The applicability of the MFB to be used as a monitor of drinking water quality and the usefulness of the organisms in this automated system was determined. Neither contaminant brought about behavioural changes in either organism that were detectable by the MFB. While extensive literature indicated that this system was useful for field applications, this study concluded that the MFB is not yet able to detect contaminants entering a water system using the above test species. Future research is required to examine other species' ability to detect aquatic contaminants and the ability of the MFB to detect such responses.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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