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Record W7109715895 · doi:10.26108/2q2r-jp39

Plant and mammal tissue cadmium concentrations in Nova Scotia and possible effects on Moose (Alces alces) populations

2002· article· en· W7109715895 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsCadmiumWillowNova scotiaFaunaMammal

Abstract

fetched live from OpenAlex

Moose (Alces alces) populations throughout much of Northeast North America have recovered from widespread extirpation, and now appear to be stable or increasing in numbers. On Mainland Nova Scotia however, moose populations are distributed at low densities and have been reduced to scattered isolated pockets of animals. Potentially high cadmium (Cd) levels in moose have raised concern over possible impacts on moose biology and human health. Moose, porcupine, and deer liver and or kidney samples were collected throughout the province in 2001-02. Willow (Salix sp.), a preferred browse species for moose, was also sampled throughout the southern half of the province. Kidney Cd concentrations were approximately 4 times higher than those in liver. The mean value for moose (ug/g) ±SE (range) was 42.1± 34.89 in the kidneys, and 10.7±9.2 in the liver. The cadmium levels measured in willow samples made it possible to establish the distribution of cadmium in the regional environment. Willow, had high cadmium concentrations, up to 7 (g/g) (dry mass) in the winter. This is above the maximum tolerable concentration for herbivores. Results of tissue analyses indicate that if humans consume moose liver and kidney they would exceed WHO standard for intake limits of cadmium per week. Findings also suggest that cadmium and its effects on flora and fauna need to be further explored, and that moose may serve as a preliminary warning of the effects of widespread environmental pollution.

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 score0.022
Threshold uncertainty score0.624

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)0.0010.000

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 head0.026
GPT teacher head0.258
Teacher spread0.232 · 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
Published2002
Admission routes1
Has abstractyes

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