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Record W2810077424

Analysis of Oncorhynchus Mykiss Tissue to Determine Lead Ion Concentration

2018· article· en· W2810077424 on OpenAlexaboutno aff
Ciaran Edwards, Bailey Hoplight, Justin Pavan

Bibliographic record

VenueDigitalCommons-IMSA (Illinois Mathematics and Science Academy) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRainbow troutLead (geology)ChemistryEnvironmental scienceBiologyFisheryFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

The variation in potential lead ion (Pb +2) concentration in various tissue samples of Oncorhynchus Mykiss (steelhead) trout caught in the Niagara River was investigated. This was used to develop a kinetic model of trace element bioaccumulation of lead in various tissues. By employing experimentally determined trace element influx and efflux from environmental food and water exposures, lead ion concentrations were determined using atomic absorption spectroscopy on homogenized fillet, liver, and gonad tissues collected from steelhead trout caught in the Niagara River. Lead ion concentrations were also determined from water samples collected at a GPS noted catch site. All sample testing was conducted under the direction of Dr. David Stewart, Ph.D. at D’Youville College Results from the study may be used to predict the level of lead ion exposure to humans through the consumption of the steelhead trout fillets and may be used predict the environmental conditions of lead accumulation in human food sources caught in local waters, such as Lake Erie, the Niagara River, and Lake Ontario. Lead measurements may suggest (a) accumulation of lead concentration in steelhead trout fillet tissue consumed by humans, and (b) processes of influx and efflux governing bioaccumulation in these animals in their natural environment, such as accumulation in the fish from ingested particles, or accumulation mostly through the food web, or that the relative accumulation varies with environmental conditions.

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 score0.075
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.050
GPT teacher head0.309
Teacher spread0.260 · 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
Published2018
Admission routes1
Has abstractyes

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