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Record W2079286350 · doi:10.1021/es9910208

A Framework for Evaluating Bioaccumulation in Food Webs

2000· article· en· W2079286350 on OpenAlexafffund
Steven Sharpe, Donald Mackay

Bibliographic record

VenueEnvironmental Science & Technology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of CanadaHealth CanadaTrent University
KeywordsOrganismFood webBioaccumulationEnvironmental scienceAbiotic componentFood chainBiochemical engineeringEcologyEnvironmental chemistryEcosystemBiologyChemistryEngineering

Abstract

fetched live from OpenAlex

As the ability to quantify contaminant uptake and clearance from individual organisms improves and modeling efforts are devoted to describing transport in increasingly complex food webs, including vegetation, there is a need to establish a consistent framework within which contaminant sources and transfer in food webs can be characterized. It is shown that a convenient and general framework is established by compiling the set of mass balance equations describing uptake in each organism by food and respiration in a matrix format using the fugacity concept. The matrices and vectors can be manipulated to gain valuable insights into how contaminant concentrations in the four abiotic media of air, water, soil, and sediment translate into concentrations in organisms throughout a food web. The framework is illustrated for a simple six organism food web including aquatic, terrestrial, and avian species. Methods of obtaining input parameters are outlined, and advantages of the approach are discussed.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations71
Published2000
Admission routes2
Has abstractyes

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