MétaCan
Menu
Back to cohort
Record W1982253494 · doi:10.1002/etc.5620191226

Mercury levels in tissues of otters from Ontario, Canada: Variation with age, sex, and location

2000· article· en· W1982253494 on OpenAlexaffabout
Gregory Mierle, Edward M. Addison, Katherine S. MacDonald, D. G. Joachim

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and ForestryMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsMercury (programming language)MustelidaeZoologyBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Concentrations of mercury in hair, brain, and liver tissues of river otter (Lutra canadensis) from several townships in Ontario, Canada, were determined. Levels of Hg in these tissues were highly intercorrelated but differed from tissue to tissue. The highest concentration was in hair, followed by liver and brain. The high correlation between Hg in hair and brain, as well as the ease and precision of Hg determinations on hair, demonstrate that Hg in hair holds considerable promise for monitoring the Hg in otters. Within the sampled otters, Hg varied with respect to age. The Hg concentrations increased over the first two to three years of age, remained stable over the next two to three years of age, and then declined in the remaining age-groups. Mean age of otters in townships with high-Hg concentrations was about half the mean age of otters in low-Hg townships. In areas where mercury levels are high, otters may have reduced survivorship because of Hg-induced stress on their health.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.994

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)0.0070.000

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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

Citations55
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Toxicology and ChemistrySame topicMercury impact and mitigation studiesFrench-language works237,207