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Record W4237109301 · doi:10.1021/cen-09546-cover3

Trout, monitors of the Great LakesTrout, monitors of the Great Lakes

2017· article· en· W4237109301 on OpenAlexaboutno aff

Bibliographic record

VenueC&EN Global Enterprise · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
Fundersnot available
KeywordsTroutSalvelinusEnvironmental sciencePollutantPetromyzonFisheryEnvironmental chemistryEnvironmental protectionFish <Actinopterygii>EcologyChemistryBiologyLamprey

Abstract

fetched live from OpenAlex

Lake trout (Salvelinus namaycush) are the largest member of the char family and occupy the top of the food web in deep, cold lakes across the upper reaches of North America. Prized by anglers, lake trout often live up to 20 years, reaching more than 60 cm in length and 10 kg in weight with high body-fat content. All these characteristics make lake trout perfect for biomonitoring because they accumulate pollutants in their bodies at levels indicative of their environmental exposure. In the Great Lakes, the world’s largest freshwater system, lake trout have been under surveillance for more than 40 years by the U.S. Environmental Protection Agency and Environment & Climate Change Canada. At the onset of monitoring in the 1970s, the focus was on the “dirty dozen” persistent organic pollutants listed by the 2001 Stockholm Convention. These recognizable compounds, now banned from production and use, include the pesticides DDT

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.622

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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