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Record W2787382421 · doi:10.1139/facets-2017-0082

Trapped river otters (<i>Lontra canadensis</i>) from central Saskatchewan differ in total and organic mercury concentrations by sex and geographic location

2018· article· en· W2787382421 on OpenAlexaffvenueabout
Steven C. Wilkie, Richard H. M. Espie, Niladri Basu, Karsten Liber, Britt D. Hall

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsMcGill UniversityUniversity of SaskatchewanMinistry of EnvironmentUniversity of Regina
Fundersnot available
KeywordsOtterMercury (programming language)LutraAnimal scienceBorealEcologyBiology

Abstract

fetched live from OpenAlex

Mercury (Hg) in wildlife remains of great concern, especially for apex piscivores. Despite this, exposure information from many species in many areas is lacking, so that management decisions are hampered. Here we examine Hg concentrations in fur, liver, and kidney tissues from river otters ( Lontra canadensis (Schreber, 1777)) ( n = 203) to quantify existing Hg concentrations over a broad geographic area in Saskatchewan. Mean fur total Hg (THg) (9.68 ± 7.52 mg/kg fresh weight (f.w.)) was significantly correlated with THg and organic Hg (OHg) in liver and kidney tissue, showcasing the potential for using fur as a noninvasive method of monitoring Hg in top-level mammals. Livers of males had higher mean OHg concentrations than livers of females (males: 2.71 mg/kg d.w., females: 1.87 mg/kg d.w.), but not significantly so. No sex-related differences were observed in kidney OHg concentrations. THg concentrations in otter fur collected in the Boreal Shield ecozone (Churchill River Upland) were significantly higher (mean = 16.1 mg/kg f.w.) than in otter fur collected from the Boreal Plain ecozone (mean = 8.59 mg/kg f.w.). Fur from otters ( n = 20; trapping block N66) trapped near a decommissioned smelter contained the highest concentrations of THg in the study (mean = 18.4 mg/kg f.w.).

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 score0.132
Threshold uncertainty score0.997

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)0.0000.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations6
Published2018
Admission routes3
Has abstractyes

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