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Record W7018144050

Contaminants in western Canadian Arctic ringed seals (Phoca hispida) : temporal variation and potential effects of a warming climate

2009· dissertation· en· W7018144050 on OpenAlexfundaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2009
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersArcticNetNatural Sciences and Engineering Research Council of CanadaFisheries Joint Management Committee
KeywordsArcticClimate changeVariation (astronomy)Global warmingThe arcticContamination
DOInot available

Abstract

fetched live from OpenAlex

I reviewed contaminant levels in ringed seals (Phoca híspida) from the western Canadian Arctic (Ulukhaktok, NT Canada) along with biological (age, sex, morphometric measurements), ecological (ðlsN, ôt'C), and sea ice parameters (mercury only).Mercury levels in muscle tissue increased with age and ð15N.Muscle mercury was significantly associated to length of the previous ice-free season (quadratic regression) between 1973- 2007 -Higher mercury concentrations as a result of short (i.e. two months) and long (i.e.five months) ice-free seasons may reflect particular environmental conditions leading to shifts in the composition and distribution of prey available to ringed seals.Temporal trends of organochlorine contaminants in blubber illustrated general decreasing levels of chlorobenzenes, hexachlorocyclohexanes, chlordanes, chlorobornanes, dichlorodiphenyl-trichloroethanes and polychlorinated biphenyls in male and female adult ringed seals.Results parallel contaminant trends in Arctic air.Organochlorine contaminants were significantly higher in male adults compared to female adults.Blubber thickness and age were also important variables in determining concentrations.and time I thank each of them.Dr. Gary Stern, who I first met in Churchill, MB and Dr. Steven Ferguson both served as encouraging co-advisors and mentors.With their help I have upgraded my problem-solving, statistical and writing skills.I will always cherish the opportunities they gave me to travel, learn, and share my research with others.Lois Harwood, whom without her expertise and data on the Ulukhaktok ringed seals the project would certainly not have been a success.She helped me to try to see the bigger picture of my research.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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 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
Published2009
Admission routes2
Has abstractno

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