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Record W107548333

Development and testing of a new bioenergetic/bioaccumulation model for persistent organic pollutants in aquatic and terrestrial food webs

2008· dissertation· en· W107548333 on OpenAlexaboutno aff
Yongshu Fan

Bibliographic record

VenueSummit (Simon Fraser University) · 2008
Typedissertation
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBioaccumulationPollutantBioenergeticsEnvironmental scienceFish <Actinopterygii>EcologyAquatic ecosystemEnvironmental chemistryBiologyFisheryChemistry
DOInot available

Abstract

fetched live from OpenAlex

This project developed and tested a bioenergetic/bioaccumulation model to predict concentrations, bioconcentration factors, bioaccumulation factors (BAF) and trophic magnification factors of persistent organic pollutants (POPs) in organisms in terrestrial and aquatic food webs. This is the first bioaccumulation model that conserves both mass of chemical and energy. This model was tested against concentrations of various POPs in wildlife of Canadian Arctic terrestrial and Lake Ontario aquatic food webs. The model is shown to predict BAFs of POPs in organisms that are in good agreement with observed BAFs as indicated by the model bias, which ranged from 0.62 to 5.31. A comparison of the behaviour of the new model to that of a previous aquatic BAF model by Arnot and Gobas (2004) shows that the models are comparable in their ability to predict BAFs. The model can be used for bioaccumulation screening of chemicals in both aquatic and terrestrial animals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.042
GPT teacher head0.217
Teacher spread0.174 · 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.

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

Citations1
Published2008
Admission routes1
Has abstractyes

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