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Record W1998964028 · doi:10.1139/a03-014

Distribution and burdens of bioaccumulative contaminants in the Lake Erie food web: A review

2003· review· en· W1998964028 on OpenAlexafffundvenue
Sarah B. Gewurtz, Miriam L. Diamond

Bibliographic record

VenueEnvironmental Reviews · 2003
Typereview
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioaccumulationFood webTrophic levelEnvironmental scienceBiotaEcologyContext (archaeology)ContaminationEutrophicationFood chainEcosystemBiologyNutrient

Abstract

fetched live from OpenAlex

The bioaccumulation of organic contaminants in the Lake Erie food web is reviewed in context of the numerous changes experienced by the system. In the late 1960s, internal lake processes, related to the eutrophic status of the lake, minimized contaminant bioaccumulation despite high contaminant loadings. From the 1970s to 1980s contaminant concentrations decreased at different rates in many species of different trophic levels, coincident with decreased loadings to the lake. Since the early 1980s contaminant concentrations in biota have not changed consistently. Several factors have been proposed to account for these patterns, including reduced nutrient loadings and productivity, and the invasion of several exotic species such as zebra mussels. These factors have altered the food web structure and the internal distribution of contaminants in the lake. Emerging and continuing issues, such as climate change, invasions of additional exotic species, new chemical contaminants of concern, and algal toxins will likely impact contaminant dynamics in the future.Key words: Lake Erie, bioaccumulative contaminants, food web.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.053
GPT teacher head0.310
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2003
Admission routes3
Has abstractyes

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